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āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Gather, analyze, and document Business Requirements related to Collections systems and regulatory impacts..
- Facilitate requirement gathering sessions with business users, IT teams, and relevant stakeholders to support system development projects.
- Perform Gap Analysis by comparing current processes (As-Is) and future processes (To-Be) to identify system enhancement opportunities..
- Coordinate with internal teams, IT, and external vendors to identify suitable solutions and ensure timely project delivery.
- Review and validate user requirements and provide recommendations to improve feasibility and process efficiency.
- Prepare batch processing plans, test scenarios, and UAT (User Acceptance Testing) scripts to ensure systems meet business requirements..
- Support troubleshooting and resolution of BAU (Business-As-Usual) system issues in collaboration with IT and users..
- Monitor and control project implementation to ensure delivery timelines and business expectations are met.
- Investigate reported system issues and coordinate with relevant teams to implement solutions.
- Support additional assignments as required.
- Bachelor s degree in Computer Science, Information Technology, Business Information Systems, or related fields.
- Minimum 2-3 years of experience as an IT Business Analyst, System Analyst, or related role..
- Experience in Business Requirement gathering, system enhancement projects, and UAT testing..
- Experience in Project Management or involvement in system implementation projects..
- Experience in Process Improvement or Lean Process initiatives is preferred..
- At least 2 years of experience working on projects within financial services, banking, or auto leasing industries is an advantage.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
SQL, MongoDB, MySQL, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Department: Information Technology.
- Company: āļāļĢāļīāļĐāļąāļ āļāļĩāđāļāđāļĄāđāļāđāļĄ āļĄāļīāļ§āļŠāļīāļ āļāļģāļāļąāļ (āļĄāļŦāļēāļāļ).
- Control and manage the company's IT infrastructure team.
- Install, manage, and maintain the data center.
- Install, manage, and maintain the company's storage and servers.
- Install, manage, and maintain the internal and external network connections.
- Install, manage, and maintain connections between cloud systems (AWS and GCP).
- Install, manage, and maintain databases using SQL Server, MongoDB, and MySQL.
- Install, manage, and maintain the company's container services using Docker.
- Install, manage, and maintain the company's mail server (Exchange, O365 Platform, and ZMail).
- Install, manage, and maintain virtualization servers.
- Install, manage, and maintain backup systems.
- Install, manage, and maintain network security systems (firewall).
- Verify and manage company licenses to ensure accuracy and compliance.
- Research and design network systems to connect core systems with new technologies.
- Collaborate with partner companies to find new solutions for improvement and development.
- Research and propose new technology recommendations to align with business needs and support future expansion.
- Collaborate with the procurement department to verify computer and related peripheral equipment purchase prices.
- Study and resolve data and errors encountered in system usage.
- Other tasks as assigned..
- Bachelor s/Master s degree in Information Technology or a related field.
- Minimum 3 years of experience in System Engineer (SE) or related roles.
- Experience using Microsoft Business Center (BC365).
- Experience with Microsoft SQL Server 201x Version.
- Experience installing, maintaining, and using Esxi, V-Center, IIS, and Microsoft family products.
- Experience with Veem Backup and Tape Backup installation, maintenance, and usage.
- Experience with Cisco family installation, maintenance, and usage.
- Good English skills in writing, reading, and speaking.
- Experience in the retail business or related fields.
- Passionate about customer service and able to work well under pressure.
- Able to work onsite 5 days a week (Flexible working hours)..
- āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ 3 āļāļĩāļāļķāđāļāđāļ.
- āļāļģāļāļ§āļ 1 āļāļąāļāļĢāļē.
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļąāļāļāļģāļāļļāļāļāđāļāļĄāļđāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđ āđāļĨāļ°āđāļĄāđāļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļ§āļĒāđāļāļāđāļāđāļĨāļĒāļĩ Machine Learning āđāļĨāļ° Data Science āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļīāļāļĨāļķāļ (Deep Insight) āđāļāļ·āđāļāļāļēāļĢāļŠāļĢāđāļēāļāđāļāļāļāļģāļĨāļāļāđāļāļāļēāļĢāļāļģāđāļāđāļāđāļāļģāļāļēāļĒ āļāļēāļāļāļēāļĢāļāđ (Predictive) āļāļāļāđāļāļāļĒāđāļāļąāļāļāļēāļāļāļļāļĢāļāļīāļāđāļŦāđāļāļąāļāļāļāļāđāļāļĢ.
- Data Modeling & ML: āļāļąāļāļāļē, āļāļāļŠāļāļ āđāļĨāļ°āļāļģ Machine Learning Models āđāļāđāļāđāļāļĢāļīāļ (Production) āđāļāļ·āđāļāđāļāđāđāļāļāļąāļāļŦāļēāļāļēāļāļāļļāļĢāļāļīāļ āđāļāđāļ āļāļēāļĢāļāļģāļāļēāļĒāļĒāļāļāļāļēāļĒ, āļāļēāļĢ Optimization, āļāļĢāļ§āļāļāļąāļāļāļļāļāļĢāļīāļ āđāļāđāļāļāđāļ.
- Advanced Analytics: āđāļāđāļ§āļīāļāļĩāļāļēāļĢāļāļēāļāļŠāļāļīāļāļīāļāļąāđāļāļŠāļđāļāđāļĨāļ°āļāļąāļĨāļāļāļĢāļīāļāļķāļĄ āđāļāļ·āđāļāļāđāļāļŦāļēāļĢāļđāļāđāļāļ (Pattern ...
- Dashboard & Reporting: āļāļąāļāļāļēāđāļĨāļ°āļāļđāđāļĨ Dashboard (Power BI) āđāļāļ·āđāļāļāļīāļāļāļēāļĄ KPI āđāļĨāļ°āļĢāļēāļĒāļāļēāļāļāļĨāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāļāļļāļĢāļāļīāļāđāļāļ Real-time.
- Business Analysis: āļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļĄāļđāļĨāđāļāļ·āđāļāļŦāļē Insight, āđāļāļ§āđāļāđāļĄ (Trends) āđāļĨāļ°āļŠāļēāđāļŦāļāļļāļāļāļāļāļąāļāļŦāļēāļāļēāļāļāļļāļĢāļāļīāļ.
- Data Interpretation: āđāļāļĨāļāļĨāļāđāļāļĄāļđāļĨāđāļŦāđāđāļāđāļāļāļģāđāļāļ°āļāļģāđāļāļīāļāļāļĨāļĒāļļāļāļāđ (Actionable Insights) āđāļāļ·āđāļāđāļāļīāđāļĄāļĒāļāļāļāļēāļĒ āļŦāļĢāļ·āļāđāļāļīāđāļĄāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļāļēāļĢāļāļģāļāļēāļ.
- Insight Communication: āļŠāļ·āđāļāļŠāļēāļĢāļāļĨāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĩāđāļāļąāļāļāđāļāļāđāļŦāđāđāļāđāļēāđāļāļāđāļēāļĒāļāđāļēāļāļāļēāļĢāļāļģ Visualization āđāļĨāļ°āļāļēāļĢāļāļģāđāļŠāļāļāļāđāļāļāļĩāļĄāļāļĢāļīāļŦāļēāļĢ.
- āļāļĢāļīāļāļāļēāļāļĢāļĩ/āđāļ āļŠāļēāļāļēāļ§āļīāļāļĒāļēāļāļēāļĢāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ, āļŠāļāļīāļāļī, āļāļāļīāļāļĻāļēāļŠāļāļĢāđ, āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļĻāļēāļŠāļāļĢāđ āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļāļāđāļēāļ Data Science/Machine Learning 1-3 āļāļĩ.
- āļŦāļĢāļ·āļ āļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļāļāđāļēāļ Data Analytics/Business Intelligence 1-3 āļāļĩ.
- āļāļąāļāļĐāļ°āļāļēāļĢāđāļāļĩāļĒāļāđāļāļĢāđāļāļĢāļĄ Python (Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch) āļāļĒāđāļēāļāļāļĩ.
- āđāļāļĩāđāļĒāļ§āļāļēāļāļāļēāļĢāđāļāđ SQL āļŠāļģāļŦāļĢāļąāļāļāļķāļāļāđāļāļĄāļđāļĨāđāļāļĢāļ°āļāļąāļāļŠāļđāļāļĄāļĩ.
- āļāļąāļāļĐāļ°āđāļāļāļēāļĢāđāļāđāđāļāļĢāļ·āđāļāļāļĄāļ·āļ Visualization (Power BI).
- āļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāļāļĢāļīāļāļāļāļēāļāļāļļāļĢāļāļīāļ (Business Acumen) āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļŠāļ·āđāļāļŠāļēāļĢāļāđāļāļĄāļđāļĨāļāļĩāđāļāļąāļāļāđāļāļāđāļāđāļāļĩ.
āļāļąāļāļĐāļ°:
SAP, Software Development
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļĢāļ§āļāļŠāļāļ āđāļĨāļ°āļāļĢāļąāļāļāļĢāļļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļĨāļēāļ āļĢāļ°āļāļāļāļēāļ SAP.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļāļđāđāđāļāđāļāļēāļ āļāļĩāļĄ IT, āļāļĢāļīāļŦāļēāļĢāļŠāļīāļāļāđāļē, āļĻāļđāļāļĒāđāļāļēāļĢāļāđāļē, āļāļąāļāļāļĩ, āļāļēāļĢāđāļāļīāļ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļāļđāđāđāļāđāļāļēāļāđāļāļĢāļ°āļāļ āđāļāļĩāđāļĒāļ§āļāļąāļāļāļąāļāļŦāļē āđāļĨāļ°āļāļēāļĢāđāļāđāđāļ.
- āļāļĢāļąāļāļāļĢāļļāļāļāļēāļĢāļāļģāļāļēāļāļāļāļāļāļđāđāđāļāđāļāļēāļāļāļēāļĄāļĢāļ°āļāļāļāļēāļ āđāļŦāđāđāļāļīāļāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāļĒāļīāđāļāļāļķāđāļ.
- āļāļāļāļēāļĢāļĻāļķāļāļĐāļēāļĢāļ°āļāļąāļ āļāļĢāļīāļāļāļēāļāļĢāļĩ āļŠāļēāļāļēāļ§āļīāļāļĒāļēāļāļēāļĢāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ āļāļāļĄāļāļīāļ§āđāļāļāļĢāđāļāļļāļĢāļāļīāļ āļŠāļēāļĢāļŠāļāđāļāļĻ āļŦāļĢāļ·āļ āļŠāļēāļāļēāļāļ·āđāļāđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļĢāļąāļāļāļĢāļļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļĨāļēāļ āļĢāļ°āļāļāļāļēāļ SAP 2 āļāļĩāļāļķāđāļāđāļ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļāļāđāļāļāļĢāļ°āļāļāļāļēāļ āļ§āļēāļāđāļāļ āļāļĢāļ°āļŠāļēāļāđāļĨāļ°āļāļīāļāļāļēāļĄāļāļēāļ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđ Software Development Life Cycle (SDLC) āđāļĨāļ° SAP.
- āļĄāļĩāļāļ§āļēāļĄāļāļīāļāļŠāļĢāđāļēāļāļŠāļĢāļĢāļāđ āđāļĨāļ°āļĢāļąāļāļāļīāļāļāļāļāđāļāļāļēāļĢāļāļāļīāļāļąāļāļīāļāļēāļ.
- āļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāđāļŦāđāļāļģāđāļāļ°āļāļģāđāļĨāļ°āļāļēāļĢāļāļīāļāļāļēāļĄāļāđāļāļĄāļđāļĨāļāđāļēāļ§āļŠāļēāļĢ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļ āļēāļĐāļēāļāļąāļāļāļĪāļĐāđāļāļĢāļ°āļāļąāļāļāļĩ.
- āļŠāļāļēāļāļāļĩāđāļāļāļīāļāļąāļāļīāļāļēāļ: āđāļāļāļ°āļĄāļāļĨāļĨāđāļĢāļēāļĄāļāļģāđāļŦāļ (āļŠāļģāļāļąāļāļāļēāļāđāļŦāļāđ), Airport Link āļĢāļēāļĄāļāļģāđāļŦāļ.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
āđāļĄāđāļāļģāđāļāđāļāļāđāļāļāļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģāļāļēāļ
āļāļąāļāļĐāļ°:
Negotiation, Thai, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ18,000 - āļŋ30,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ, āļĄāļĩāļāđāļēāļāļāļĄāļĄāļīāļāļāļąāđāļ
- Present and sell commercial and industrial washing machines, dryers, and related laundry equipment..
- Develop new customers and maintain relationships with existing clients such as laundromat investors, hotels, hospitals, factories, and commercial laundries..
- Provide professional consultation on machine selection, shop layout, and laundry solutions..
- Prepare quotations, negotiate terms, and close sales deals..
- Coordinate with technical, installation, and after-sales service teams..
- Follow up on order status, delivery, and customer satisfaction..
- Prepare sales reports and update customer information..
- Visit customers on-site and attend exhibitions or trade shows when required..
- __________________________________.
- Strong communication, negotiation, and closing skills..
- Self-motivated, target-driven, and able to work under pressure..
- Own a car and able to travel upcountry when required..
- Basic computer skills (Line, Excel, Google Docs, Email, Socialmedia relative skill)..
- __________________________________.
- Compensation & Benefits.
- Salary.
- Commission (uncapped, based on sales performance).
- Travel and phone allowance (as per company policy).
- Social Security.
- Performance bonus.
- Product and sales training provided.
- Work Style & Career Growth.
- Proactive sales role (Active / Hunter Sales).
- High income opportunity based on performance.
- Career growth to Senior Sales or Sales Manager.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŋ30,000 - āļŋ50,000, āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļđāđāļĨāļĢāļ°āļāļ Network āđāļĨāļ° Server Infrastructure.
- āļāļĢāļīāļŦāļēāļĢ Linux Server, Proxmox, Docker.
- āļāļđāđāļĨ Web / Database / Email / Name Server.
- āđāļāđāđāļāļāļąāļāļŦāļē Hosting, SSL, VoIP āđāļĨāļ°āļĢāļ°āļāļāđāļāđāļĄāļ.
- Support āļĨāļđāļāļāđāļēāļāđāļēāļāđāļāļāļāļīāļāļāļĒāđāļēāļāļĄāļ·āļāļāļēāļāļĩāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđ Linux Server āđāļĨāļ° Network Infrastructure.
- āđāļāđāļāļēāļ Proxmox, Docker āđāļĨāļ° Database (MySQL/MariaDB) āđāļāđ.
- āđāļāđāļēāđāļ Web Server (Apache/Nginx) āđāļĨāļ°āļĢāļ°āļāļāđāļāđāļĄāļ.
- āđāļāđāđāļāļāļąāļāļŦāļē Hosting, Email, SSL, VoIP āđāļāđ.
- āļŠāļ·āđāļāļŠāļēāļĢāļāļĩ āļĢāļąāļāļāļēāļĢāđāļāđāļāļąāļāļŦāļē āđāļĨāļ°āļāļģāļāļēāļāļ āļēāļĒāđāļāđāđāļĢāļāļāļāļāļąāļāđāļāđ.
- āļāļģāđāļĄāļāđāļāļ DotArai?.
- āđāļāđāļāļģāļāļēāļāļāļąāļāļĢāļ°āļāļāļāļĢāļīāļāļāļĩāđāļāđāļēāļāļēāļĒ.
- āļāļĩāļĄāļāļēāļāļĄāļ·āļāļāļēāļāļĩāļ āļāļĢāđāļāļĄāļāļąāļāļāļāļĢāđāļ.
- āđāļāļāļēāļŠāđāļāļīāļāđāļāđāļāļŠāļēāļĒ Infrastructure & DevOps.
āļāļąāļāļĐāļ°:
Purchasing, Microsoft Office
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļāļąāļāļŦāļēāļŠāļīāļāļāđāļē/āļāļĢāļīāļāļēāļĢ āļāļĢāļāļāđāļ§āļ āļāļđāļāļāđāļāļāļāļēāļĄāļāļāļāđāļāļāļāļēāļ āđāļĨāļ°āđāļāđāļāđāļāļāļēāļĄāđāļāđāļēāļŦāļĄāļēāļĒāļāđāļēāļāļāđāļāļāļļāļ āļāļļāļāļ āļēāļ āđāļĨāļ°āļāļēāļĢāļŠāđāļāļĄāļāļ.
- āļāļąāļāļŦāļēāļŠāļīāļāļāđāļē/āļāļĢāļīāļāļēāļĢ āđāļŦāđāļāļĒāļđāđāļ āļēāļĒāđāļāļĢāļ°āļĒāļ°āđāļ§āļĨāļē (Processing Time) āļāļĩāđāļāļģāļŦāļāļ.
- āļāļąāļāļŦāļēāļāļąāļāļāļĨāļēāļĒāđāļāļāļĢāđ āļāļĢāđāļāļĄāļāļąāđāļ āļāļąāļāđāļĨāļ·āļāļ āļāļĢāļ°āđāļĄāļīāļāļāļđāđāļāļēāļĒāļĢāļēāļĒāđāļŦāļĄāđ āđāļāļ·āđāļāđāļāđāđāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļāļąāļāļāļ·āđāļ āđāļĨāļ°āļāđāļāļĢāļāļāļĢāļēāļāļē āđāļāļ·āđāļāđāļŦāđāđāļāđāļĢāļēāļāļēāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđ āđāļāļĢāļĩāļĒāļāđāļāļĩāļĒāļāļĢāļēāļāļē āđāļāļ·āđāļāđāļāđāđāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāļāļąāļāļāļ·āđāļ āđāļĨāļ°āļāđāļāļĢāļāļāļĢāļēāļāļē āļāļĢāđāļāļĄāļāļąāđāļ āļāļąāļāđāļĨāļ·āļāļāļāļđāđāļāļēāļĒ āđāļāļ·āđāļāđāļŦāđāđāļāđāļĢāļēāļāļēāļāļĩāđāđāļŦāļĄāļēāļ°āļŠāļĄ.
- āļŠāļĢāļĢāļŦāļē āļāļąāļāļāļĨāļēāļĒāđāļāļāļĢāđ āđāļĨāļ°āđāļŦāļĨāđāļāļāļ·āđāļāļŠāļīāļāļāđāļē āļāļļāļāļāļĢāļāđ āđāļŦāļĄāđāđ āļāļąāđāļāđāļāļāļĢāļ°āđāļāļĻāđāļĨāļ°āļāđāļēāļāļāļĢāļ°āđāļāļĻ.
- āļāļąāļāļāļģāđāļāļŠāļąāđāļāļāļ·āđāļ/āļāđāļēāļ āđāļĨāļ°āļŠāļąāļāļāļēāļāđāļēāļāđ āđāļŦāđāļāļđāļāļāđāļāļ āļāļģāđāļāļīāļāļāļēāļĢāđāļĨāļ°āļāļīāļāļāļēāļĄāļāļēāļĢāļāļģāļŠāļąāļāļāļēāļāļ·āđāļāļāļēāļĒ āļŠāļīāļāļāđāļē/āļāļĢāļīāļāļēāļĢ/āļāļēāļāļ§āđāļēāļāđāļēāļ āđāļŦāđāļĨāļāļāļēāļĄāļāļĢāļāļāđāļ§āļāļŠāļĄāļāļđāļĢāļāđ āļāļĢāđāļāļĄāļŦāļĨāļąāļāļāļĢāļ°āļāļąāļ.
- āļāļąāļāļāļģāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāđāļāļāļļāļāđāļŦāļĄāđ āđāļĨāļ° āļŦāļēāđāļāļ§āļāļēāļāđāļāļāļēāļĢāļĨāļāļāđāļāļāļļāļ.
- āđāļāļĢāļāļēāļāđāļāļĢāļāļāļŠāļąāļāļāļē āļāļĢāļąāļāļāļĢāļļāļāļĢāļēāļāļē āđāļĨāļ°āđāļāļ·āđāļāļāđāļāļāļąāļāļāļēāļāļāļąāļāļāļĨāļēāļĒāđāļāļāļĢāđ āļĢāļ§āļĄāļāļķāļ āļāļĢāļīāļŦāļēāļĢāļāļēāļĢāļāļąāļāļāļ·āđāļ/āļāđāļēāļ āđāļĨāļ°āļāļāļāļ§āļāđāļāļāļēāļŠāļāļēāļāļāļļāļĢāļāļīāļāđāļāļĒāđāļāđāļāļēāļĢāđāļāļĢāļāļēāļāđāļāļĢāļāļ āđāļāļĢāļ·āđāļāļāļĄāļ·āļāđāļĨāļ°āļ§āļīāļāļĩāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļĢ āđāļŦāđāļŠāļēāļĄāļēāļĢāļāļāļĢāļīāļŦāļēāļĢāļāļēāļĢāļāļąāļāļāļ·āđāļ/āļāđāļēāļāļāđāļģāļāļ§āđāļēāļŦāļĢāļ·āļāļāļĒāļđāđāđāļāļāļāļāļĢāļ°āļĄāļēāļ (Cost Reduction).
- āļāļģāļŦāļāđāļēāļāļĩāđāļāļīāļāļāđāļ āļāļĢāļ°āļŠāļēāļāļāļēāļ āļŠāļāļąāļāļŠāļāļļāļāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļąāļāļāļ·āđāļ āđāļāļĢāļāļāļēāļĢāđāļĨāļ°āļāļīāļāļāļĢāļĢāļĄāđāļŦāļĄāđ āļāļēāļāļŦāļāđāļ§āļĒāļāļēāļāļāļ·āđāļāđ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļĨāļ° āļāļąāļāļāļĨāļēāļĒāđāļāļāļĢāđ.
- āļĢāđāļ§āļĄāļāļąāļāļāļē āļŠāļ·āđāļāļŠāļēāļĢ āđāļĨāļ°āļāļąāļāļāļēāļĢāļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđāļāļąāļāļāļąāļāļāļĨāļēāļĒāđāļāļāļĢāđ āđāļāļ·āđāļāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļĢāļāļĩāđāļŠāļāļāļāļĨāđāļāļāļāļąāļ āļĢāļ§āļĄāļāļķāļ āļĢāđāļ§āļĄāļāļąāļāļāļēāđāļŦāđāļāļđāđāļāđāļēāļāļāļīāļāļąāļāļīāļāļēāļĄāļāļĢāļĢāļĒāļēāļāļĢāļĢāļāļāļđāđāļāđāļēāļŊ.
- āļĻāļķāļāļĐāļēāļāđāļāļĄāļđāļĨāļŠāļāļēāļāļāļēāļĢāļāđāđāļāļ§āđāļāđāļĄāļĢāļēāļāļē āđāļĨāļ°āļŠāļāļēāļāļāļēāļĢāļāđāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāđāļāļāļĨāļēāļāļāļāļāļŠāļīāļāļāđāļē āđāļĨāļ°āļāļĢāļīāļāļēāļĢāļāļĩāđāļĄāļĩāļāļĨāļāđāļāļāļĢāļ°āļāļ§āļāļāļēāļĢāļāļąāļāļāļ·āđāļ.
- āļŠāļāļąāļāļŠāļāļļāļ āļāļēāļ Turnaround Maintenance āđāļŦāđāļāļģāđāļāļīāļāļāļēāļĢāđāļĨāđāļ§āđāļŠāļĢāđāļāļāļĒāđāļēāļāļĢāļēāļāļĢāļ·āđāļ āļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ.
- āļāļĢāļīāļāļāļēāļāļĢāļĩ āļŦāļĢāļ·āļ āļāļĢāļīāļāļāļēāđāļ āļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļĻāļēāļŠāļāļĢāđ /āļ§āļīāļāļĒāļēāļĻāļēāļŠāļāļĢāđ /āļāļĢāļīāļŦāļēāļĢāļāļļāļĢāļāļīāļ/ āļāļĢāļīāļŦāļēāļĢāļāļąāļāļāļēāļĢ Supply Chain āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ 3-5 āļāļĩ āđāļāļŠāļēāļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāđāļāļĩāđāļĒāļ§āļāļąāļāļŠāļīāļāļāđāļēāđāļĨāļ°āļāļĢāļīāļāļēāļĢāļŠāļģāļŦāļĢāļąāļāļāļēāļāļāļąāļāļŦāļē āļĢāļ§āļĄāļāļķāļāļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļāļĢāļ°āđāļāļĩāļĒāļāļāļĢāļīāļĐāļąāļ āļāļāļŦāļĄāļēāļĒ āđāļĨāļ°āļ āļēāļĐāļĩ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļēāļāļāļ·āđāļāļāļēāļĒāđāļĨāļ°āļ§āđāļēāļāđāļēāļ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāđāļēāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļāļēāļāļāļąāļāļŦāļēāļāļąāļŠāļāļļ āļāļēāļĢāđāļāļĢāļāļēāļāđāļāļĢāļāļ āđāļĨāļ°āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢāļāļĩāđāļāļĩ āļŠāļēāļĄāļēāļĢāļāļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ.
- āļŠāļēāļĄāļēāļĢāļāļŠāļ·āđāļāļŠāļēāļĢāļ āļēāļĐāļēāļāļąāļāļāļĪāļĐāđāļāđāļāļĩ (TOEIC 700 āļāļ°āđāļāļ).
- āļĄāļĩāļāļ§āļēāļĄāļāļģāļāļēāļāđāļāļāļēāļĢāđāļāđ āļāļąāļāļĐāļ°āļāđāļēāļāļāļēāļĢāđāļāđāļāļēāļ computer āđāļāļĒāđāļāļāļēāļ° āļāđāļēāļ Microsoft office āđāļāđāļāļĒāđāļēāļāļāļĩ Certificate SCM,CIPS (āļāđāļēāļĄāļĩ).
āļāļąāļāļĐāļ°:
Electrical Engineering, Petrochemical
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Act as RCM Facilitator for non static equipment (rotating and electrical)..
- Develop, maintain, and update Equipment Strategies, including short- and long-term maintenance plans, operating strategies, and spare parts requirements..
- Steward and track the execution of all action items arising from Equipment Strategy and RCM studies.
- Work closely with operations, maintenance, and plant engineering teams to ensure effective implementation of reliability initiatives.
- Bachelor s degree in electrical engineering..
- Minimum GPA 2.80, Minimum TOEIC score 650.
- Experience in plant engineering and/or maintenance in an industrial or process plant environment is an advantage..
- Strong knowledge of equipment reliability, maintenance strategy development, and asset integrity principles.
- Preferred Qualifications.
- Formal RCM Facilitator training and/or hands on experience facilitating RCM studies in an industrial plant (highly advantageous)..
- Experience working in oil refinery, petrochemical, power, or heavy industrial facilities..
- Strong coordination, analytical, and communication skills with the ability to drive actions to completion.
āļāļąāļāļĐāļ°:
ETL, Compliance, SQL
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Design, implement, and manage end-to-end data pipelines architectures.
- Configure and maintain data ingest workflows (ETL) across several production systems.
- Transform data into Data Mart, Data Model that can be easily analyzed.
- Ensure data accuracy, high usability, timely availability, and strong performance.
- Demonstrate a hands-on development mindset with a willingness to troubleshoot and solve complex problems.
- Ensure compliance with data governance and security policies.
- Minimum of 3 years of work experience as a Data Engineer.
- Strong SQL skills with knowledge of NoSQL tools and languages.
- Strong proficiency in Python scripting.
- Experience with AWS Cloud Data Platform services such as S3, Redshift, Glue, Step Functions, and Lambda.
- Experience with other cloud data platforms such as GCP or Azure is an advantage.
- Experience working on Big Data platform is an advantage.
- Strong business understanding, with the ability to identify business problems, define business goals, and locate relevant data.
āļāļąāļāļĐāļ°:
Java, Linux, UNIX, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor's degree or higher in Computer Science, Computer Engineering, Information Technology, or a related field.
- Strong proficiency in Java programming language.
- Experience in rest full API Design.
- Familiarity with Linux/Unix operating system.
- Familiarity with relational databases and SQL.
- At least 5 years of experience in Software Engineer.
- At least 2-3 years of experience in senior role.
- Strong problem-solving and analytical skills.
- Familiarity with Agile methodology and CI/CD pipelines.
- Excellent communication and collaboration abilities.
- Capability to collaborate with several other developers and mentor junior team members.
- Ability to work under pressure in production environments.
- Strong ownership and accountability mindset.
- Good English communication skill.
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļĻāļķāļāļĐāļēāđāļĨāļ°āļāļģāđāļāļīāļāļāļēāļāļ§āļīāļĻāļ§āļāļĢāļĢāļĄ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļļāļāļāļĢāļāđāļāļēāļĢāļāļĨāļąāđāļ (Static Equipment) āļĢāļ°āļāļāļāđāļāļāļēāļ (Piping) āđāļāļĢāļāļŠāļĢāđāļēāļāđāļĨāļ°āđāļĒāļāļē (Structural and Civil) āđāļŦāđāđāļāđāļāđāļāļāļēāļĄāđāļāļāļāļēāļ āļĄāļēāļāļĢāļāļēāļāļŠāļēāļāļĨ (Code/Standard) āđāļĨāļ°āļāđāļāļāļāļŦāļĄāļēāļĒāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ.
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļĢāļ°āđāļĄāļīāļāļŠāļ āļēāļāļāļ§āļēāļĄāļŠāļĄāļāļđāļĢāļāđāļāļāļāļāļļāļāļāļĢāļāđ āļāļēāļāļāļĨāļāļēāļĢāļāļĢāļ§āļāļŠāļāļ āļāđāļāļĄāļđāļĨāļāļēāļĢāđāļāđāļāļēāļ āđāļĨāļ°āļāļĢāļ°āļ§āļąāļāļīāļāļ§āļēāļĄāđāļŠāļĩāļĒāļŦāļēāļĒ āđāļāļ·āđāļāļāļąāļāļāļģāļāđāļāđāļŠāļāļāđāļāļ°āļāļēāļāļ§āļīāļĻāļ§āļāļĢāļĢāļĄāđāļāļāļēāļĢāļāđāļāļĄāđāļāļĄ āļāļĢāļąāļāļāļĢāļļāļ āļŦāļĢāļ·āļāļāđāļāļāļāļąāļāļāļ§āļēāļĄāđāļŠāļĩāļĒāļŦāļēāļĒ.
- āļŠāļāļąāļāļŠāļāļļāļāđāļĨāļ°āļĄāļĩāļŠāđāļ§āļāļĢāđāļ§āļĄāđāļāļāļēāļĢāļāļąāļāļāļģāđāļāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļļāļāļāļĢāļāđ āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļāļīāļāļāļēāļĄāļāļĨāļāļēāļĢāļāļģāđāļāļīāļāļāļēāļāļāļēāļĄ ...
- āļŠāļāļąāļāļŠāļāļļāļāļāļēāļāļāđāļēāļāļ§āļīāļĻāļ§āļāļĢāļĢāļĄāđāļĨāļ°āļāļēāļĢāļāļģāđāļāļīāļāđāļāļĢāļāļāļēāļĢ āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļ Static Equipment, Piping āđāļĨāļ° Civil āđāļāļĒāļĢāļąāļāļāļīāļāļāļāļāļāđāļēāļāđāļāļāļāļīāļāļāļēāļĄāļāļĩāđāđāļāđāļĢāļąāļāļĄāļāļāļŦāļĄāļēāļĒ.
- āđāļāđāļēāļĢāđāļ§āļĄāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāļŠāļēāđāļŦāļāļļāļāļ§āļēāļĄāđāļŠāļĩāļĒāļŦāļēāļĒāļāļāļāļāļļāļāļāļĢāļāđ (RCFA) āđāļĨāļ°āļāļīāļāļāļēāļĄāļāļēāļĢāļāļąāļāļāļĢāđāļāļ (Corrosion Monitoring) āđāļāļ·āđāļāđāļāļīāđāļĄāļāļ§āļēāļĄāđāļāļ·āđāļāļāļ·āļāđāļāđāđāļĨāļ°āļĨāļāļāļēāļĢāđāļāļīāļāđāļŦāļāļļāļāđāļģ.
- āđāļŦāđāļāļģāļāļĢāļķāļāļĐāļē āđāļāļ°āļāļģ āđāļĨāļ°āļāđāļēāļĒāļāļāļāļāļ§āļēāļĄāļĢāļđāđāļāđāļēāļāđāļāļāļāļīāļāđāļāđāļāļāļąāļāļāļēāļāļāļĢāļ§āļāļŠāļāļāđāļĨāļ°āļāļđāđāļāļĢāļ§āļāļŠāļāļ āđāļāļ·āđāļāļĒāļāļĢāļ°āļāļąāļāļāļļāļāļ āļēāļāļāļēāļāđāļĨāļ°āļāļąāļāļāļēāļĻāļąāļāļĒāļ āļēāļāļāļāļāļāļĩāļĄāļāļēāļ.
- āļāļ§āļāļāļļāļĄ āļāļđāđāļĨ āđāļĨāļ°āļāļąāļāļāļģāđāļāļāļŠāļēāļĢāļāļēāļāļ§āļīāļĻāļ§āļāļĢāļĢāļĄāđāļĨāļ°āļĢāļ°āļāļāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļŦāđāļĄāļĩāļāļ§āļēāļĄāļāļđāļāļāđāļāļ āđāļāđāļāļāļąāļāļāļļāļāļąāļ āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļāļĢāļ§āļāļŠāļāļāļĒāđāļāļāļŦāļĨāļąāļāđāļāđ.
- āļāļāļīāļāļąāļāļīāļāļēāļāđāļĨāļ°āļŠāđāļāđāļŠāļĢāļīāļĄāļāđāļēāļāļāļēāļāļĩāļ§āļāļāļēāļĄāļąāļĒ āļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒ āļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄ āđāļĨāļ°āļāļĨāļąāļāļāļēāļ (SHEE) āđāļŦāđāļŠāļāļāļāļĨāđāļāļāļāļąāļāļāđāļĒāļāļēāļĒāđāļĨāļ°āļāđāļāļāļģāļŦāļāļāļāļāļāļāļĢāļīāļĐāļąāļ.
- āļŠāļģāđāļĢāđāļāļāļēāļĢāļĻāļķāļāļĐāļēāļĢāļ°āļāļąāļāļāļĢāļīāļāļāļēāļāļĢāļĩāļŦāļĢāļ·āļāļāļĢāļīāļāļāļēāđāļāļāļēāļāļāđāļēāļāļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļĻāļēāļāļĢāđāļŠāļēāļāļēāđāļāļĢāļ·āđāļāļāļāļĨ.
- GPAX āđāļĄāđāļāđāļģāļāļ§āđāļē 2.80 āļāļ°āđāļāļāļāļāļŠāļāļāļ āļēāļĐāļēāļāļąāļāļāļĪāļĐ (TOEIC) āđāļĄāđāļāđāļģāļāļ§āđāļē 600 āļāļ°āđāļāļ.
- āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđ āļāļ§āļēāļĄāļĢāļđāđ āļāļ§āļēāļĄāļāļģāļāļēāļ āđāļāļāļēāļāļāđāļēāļāđāļāļĢāļ·āđāļāļāļāļĨ āļāļļāļāļāļĢāļāđāļāļēāļĢāļāļĨāļąāđāļ āļĢāļ°āļāļāļāđāļāļāļēāļ āđāļāļĢāļāļŠāļĢāđāļēāļāđāļĨāļ°āđāļĒāļāļē āļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļļāļāļŠāļēāļŦāļāļĢāļĢāļĄāļāļīāđāļāļĢāđāļĨāļĩāļĒāļĄāļŦāļĢāļ·āļāļāļīāđāļāļĢāđāļāļĄāļĩ 0-5 āļāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđ āļāļąāļāļĐāļ°āđāļāļĩāđāļĒāļ§āļāļąāļāļāļēāļāļāļāļāđāļāļ āļāļīāļāļāļąāđāļ āļāļĢāļ§āļāļŠāļāļ āļŦāļĢāļ·āļāļāđāļāļĄāļāļģāļĢāļļāļāļāļļāļāļāļĢāļāđāļāļēāļĢāļāļĨāļąāđāļāļāļĢāļ°āđāļ āļ Static āđāļĨāļ°āļĢāļ°āļāļāļāđāļāļāļēāļ (Piping) āđāļāļāļļāļāļŠāļēāļŦāļāļĢāļĢāļĄāļāļīāđāļāļĢāđāļĨāļĩāļĒāļĄāļŦāļĢāļ·āļāļāļīāđāļāļĢāđāļāļĄāļĩ āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļģāļāļēāļāđāļāđāļāļāļĩāļĄāļāđāļ§āļĒāđāļāļāļĢāļīāļāļēāļĢ āļĄāļĩāļāļ§āļēāļĄāđāļāđāļāļāļđāđāļāļģ āļĄāļļāđāļāļĄāļąāđāļāļāļļāļāļīāļĻāļāļāđāļāļ·āđāļāļāļāļāđāļāļĢ āđāļāđāļĢāļđāđ āļŠāļēāļĄāļēāļĢāļāļāļĢāļąāļāļāļąāļ§āđāļĨāļ°āļŠāļĢāđāļēāļāļŠāļĢāļĢāļāđāļŠāļīāđāļāđāļŦāļĄāđ āļāļĢāļ°āļŦāļāļąāļāļāļķāļāļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒ āļāļēāļāļĩāļ§āļāļāļēāļĄāļąāļĒ āļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄāđāļĨāļ°āļŠāļąāļāļāļĄ.
- āļŦāļēāļāļĄāļĩāđāļāļāļĢāļ°āļāļāļāļ§āļīāļāļēāļāļĩāļāļ§āļīāļĻāļ§āļāļĢāļĢāļĄ (āļ.āļ§.) āļŠāļēāļāļēāđāļāļĢāļ·āđāļāļāļāļĨ āļŦāļĢāļ·āļāļŠāļēāļāļēāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āļāļ°āđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāđāļāđāļāļāļīāđāļĻāļĐ.
- āļĒāļīāļāļāļĩāļĢāļąāļāļāļąāļāļĻāļķāļāļĐāļēāļāļāđāļŦāļĄāđ.
āļāļąāļāļĐāļ°:
ETL, Big Data
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Pipeline Development: Support the design and maintenance of scalable ETL/ELT pipelines for structured and unstructured datasets..
- AI Data Readiness: Assist in building data ingestion flows for Vector Databases and supporting RAG (Retrieval-Augmented Generation) architectures..
- Data Modeling: Contribute to the creation of robust data models and Feature Stores that serve both traditional analytics and machine learning workloads..
- Data Quality & Operations.
- Validation & Cleaning: Implement automated scripts to ensure high data integrity, reliability, and performance across the platform..
- Cloud Optimization: Assist in monitoring cloud resource usage (GCP/AWS) to ensure cost-efficiency and low-latency data access..
- Engineering Collaboration: Work closely with senior engineers to document data lineage and ensure the architecture is built for long-term scalability..
- Experience: Entry-level to 2 years of experience in data engineering, backend development, or a related technical internship.
- Portfolio: Demonstration of coding ability through a Github repository or a portfolio of data projects (e.g., building a personal API, a data scraper, or a small-scale ETL project)..
- Education: Bachelor s degree in Computer Engineering, Computer Science, Statistics, or a related technical field..
- Python: Solid foundation in writing clean, modular Python code.
- SQL: Proficiency in writing and optimizing complex queries for data analysis.
- Cloud Knowledge: Familiarity with at least one major cloud provider (GCP or AWS) and basic understanding of services like BigQuery, Redshift, or S3.
- AI Awareness: Interest in how data engineering supports AI; basic knowledge of unstructured data or vector search is a plus..
- What We Offer.
- Hands-on Multi-Cloud Experience: Get direct exposure to large-scale data environments on GCP/AWS and modern orchestration tools..
- Innovative Tech Stack: Work with cutting-edge tools at the intersection of Big Data and AI, including Vector DBs and automated data quality frameworks..
- Growth & Mentorship: A supportive environment where you will learn from senior engineers and have a clear path for professional development..
- Impactful Work: See your data pipelines directly power real-time marketing decisions and AI-driven products..
- If you re ready to build the data foundation for the next generation of AI, apply now!.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
RESTful, Scrum, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Analyze business processes and translate business needs into clear system requirements and technical solutions.
- Gather, define, validate, and manage system requirements in collaboration with business stakeholders and cross-functional teams.
- Evaluate existing systems and propose enhancements to improve performance, scalability, and maintainability.
- Design end-to-end system architecture, workflows, and solutions covering both functional and non-functional requirements.
- Select and design appropriate technologies aligned with system architecture and business objectives.
- Design and oversee front-end and back-end system components, including creating system flows, wireframes, and prototypes.
- Perform impact analysis, support unit testing, and drive system optimization initiatives.
- Design, develop, and maintain RESTful APIs and microservices architecture with proper governance and documentation standards.
- Create and maintain comprehensive system documentation, including SRS, DFD, architecture diagrams, program specifications, workflow diagrams, user manuals, and technical documentation.
- Collaborate and coordinate with users, vendors, developers, testers, and other stakeholders to ensure successful delivery.
- Work with infrastructure teams on Azure cloud architecture, deployment design, firewall considerations, and system integration.
- Partner closely with development and QA teams to ensure solution alignment and delivery quality.
- Analyze, troubleshoot, and resolve system issues in a timely and effective manner.
- Bachelor s or Master s degree in Computer Science, Information Technology, or related field.
- 3 - 5 years of experience as a System Analyst, or related role in enterprise environments.
- Strong understanding of system design, software architecture, and integration patterns (e.g., APIs, microservices).
- Hands-on experience with RESTful APIs, cloud platforms (preferably Azure), and modern application architectures.
- Experience in creating system documentation such as SRS, DFD, and architecture diagrams.
- Familiarity with Agile/Scrum methodologies and SDLC processes.
- Strong analytical, problem-solving, and troubleshooting skills.
- Excellent communication skills with the ability to work with both technical and non-technical stakeholders.
- Ability to manage multiple tasks and work effectively in a fast-paced environment.
- Thai Native level communication with good command in English.
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Data Engineer Manager āļĄāļĩāļāļāļāļēāļāļŠāļģāļāļąāļāđāļāļāļēāļĢāļāļāļāđāļāļ āļāļąāļāļāļē āđāļĨāļ°āļāļĢāļīāļŦāļēāļĢāļāļąāļāļāļēāļĢāđāļāļĢāļāļŠāļĢāđāļēāļāļāļ·āđāļāļāļēāļāļāđāļēāļāļāđāļāļĄāļđāļĨ (Data Infrastructure) āļāļāļāļāļāļāđāļāļĢ āđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāļēāļĢāđāļāđāļāļēāļāļāđāļēāļ Analytics, AI āđāļĨāļ° Business Intelligence āđāļāļĒāļāļģāļŦāļāđāļēāļāļĩāđāļāļģāļāļĩāļĄ Data Engineer āđāļĨāļ°āļāļģāļāļēāļāļĢāđāļ§āļĄāļāļąāļāļāļĩāļĄ Data, Product āđāļĨāļ° Business āđāļāļ·āđāļāļŠāđāļāļĄāļāļāđāļāļĨāļđāļāļąāļāļāđāļāļĄāļđāļĨāļāļĩāđāļĄāļĩāļāļļāļāļ āļēāļ āļĄāļĩāđāļŠāļāļĩāļĒāļĢāļ āļēāļ āđāļĨāļ°āļŠāļēāļĄāļēāļĢāļāļāļĒāļēāļĒāđāļāđ (Scalable & Reliable).
- Data Architecture & Engineering
- āļāļāļāđāļāļāđāļĨāļ°āļāļąāļāļāļē Data Architecture (Data Lake / Data Warehouse / Data Pipeline)
- āļŠāļĢāđāļēāļāđāļĨāļ°āļāļđāđāļĨ ETL / ELT Pipelines āđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāđāļāļĄāļđāļĨāļāļēāļāļŦāļĨāļēāļĒāđāļŦāļĨāđāļ (Structured / Unstructured)
- āļāļąāļāļāļē Data Models āđāļāļ·āđāļāļĢāļāļāļĢāļąāļāļāļēāļĢāđāļāđāļāļēāļāļāđāļēāļ Analytics āđāļĨāļ° Reporting.
- Experience - āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ 7-12 āļāļĩ āđāļāļāđāļēāļ Data Engineering / Data Platform - āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļĢāļīāļŦāļēāļĢāļāļĩāļĄ (2-5 āļāļĩāļāļķāđāļāđāļ) - āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāļģ Data Pipeline āļāļāļēāļāđāļŦāļāđ āļŦāļĢāļ·āļāļĢāļ°āļāļāļāļĩāđāļĄāļĩ Data Volume āļŠāļđāļ ________________________________________.
- Technical Skills - Programming: Python / SQL (Advanced) - Big Data Tools: Spark, Hadoop, Kafka - Workflow Tools: Airflow, DBT - Cloud Platform: AWS / Azure / GCP - Data Warehouse: BigQuery, Snowflake, Redshift ________________________________________.
- Skills & Competencies - Leadership & People Management - Problem Solving & Analytical Thinking - Stakeholder Management - Communication Skills (Technical & Non-technical) - Project / Delivery Management ________________________________________.
- Preferred Qualifications - āļĄāļĩāļāļĢāļ°āļŠāļāļāļēāļĢāļāđāļāđāļēāļ Real-time Data / Streaming - āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāļāđāļēāļ Data Governance / Data Security - āļĄāļĩ Certification āļāđāļēāļ Cloud āļŦāļĢāļ·āļ Data Engineering.
- Contact Information K. Nanchanok (Recruiter)
- Email: nanchanok.r @thaibev.com
- Company name: DIGITAL AND TECHNOLOGY SERVICES CO., LTD.
- Working Location and address: F.Y.I Center 2525 Rama IV Rd, Khlong Tan, Khlong Toei, Bangkok 10110
- MRT QSNCC Station Exit 1.
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
7 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Power point, Problem Solving, Statistics, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Emails to members of the Working Group.
- Bilateral calls with members, as needed, to prepare for meeting.
- Agendas for meetings of the Working Group.
- One per quarter until end of contract.
- 31 August 2026.
- 30 November 2026.
- 28 February 2027.
- 31 May 2027.
- 2Reach out to additional stakeholders who may be interested in participating in meetings, whether regularly or on an ad-hoc basis based on needs.
- Emails to additional stakeholders that may wish to participate in the meetings, whether regularly or on an ad-hoc basis based on expertise needed.
- Calls to additional stakeholders to encourage participation, as needed.
- 10 June 2026 3Prepare presentations and materials and co-facilitate the working group meetings.
- Presentations and other supporting materials for the meetings of the Working Group (quarterly meetings, for the duration of the consultancy).
- Co-facilitation of meetings of the working group, with focus on technical inputs.
- One per quarter until end of contract.
- 31 August 2026.
- 30 November 2026.
- 28 February 2027.
- 31 May 2027.
- 4 Draft short (1 page) summaries of key decisions made during meetings of the Working Group. Finalized one page summaries of key decisions from Working Group meetings.
- One per quarter until end of contract.
- 31 August 2026.
- 30 November 2026.
- 28 February 2027.
- 31 May 2027.
- 5 Making use of macro and microdata, conduct data integration and analysis to facilitate the creation of mock account tables that shed light on possibilities and challenges to implement the SEEA from a LNOB perspective.Integrated datasets.
- Mock account tables for at least two countries.
- Collation of feedback from members of the Group regarding the table calculation.
- 31 December 2026.
- 6 Liaise with Member States and other members of the Group to encourage them to calculate account tables with their own national data. Provide guidance as needed, including capacity building and technical support to countries, and through in-person missions if needed.Emails to NSOs to support the calculation of account tables.
- Online meetings with NSOs to support the calculation of account tables.
- Power Point presentations providing guidance on how to calculate account tables.
- In-person events, where needed, to support countries with the calculation of account tables.
- 31 March 2027 7 Collate these experiences (including descriptions of the calculation process) and reflect them in a report of the Group. In the report, include a section with step-by step information that may facilitate the replication of calculations as needed.Draft report including descriptions of the calculation process and step by step guidance for countries.
- Relevant feedback received from group members and UN Women.
- 1 May 2027.
- 8 Draft a set of recommendations for the integration of an LNOB perspective in the implementation of the SEEA.Set of recommendations with hands-on guidance for the integration of an LNOB perspective in the implementation of the SEEA (conclusions of the report).
- Incorporate feedback from Group members on such recommendations.
- 31 May 2027 9 Liaise with key global and regional partners to promote the uptake of the Group s recommendations. This should include liaison with global processes such as the revision of the SEEA CF, among others.Emails with key global stakeholders to promote uptake.
- Presentations at online or in-person meetings as appropriate, if these are taking place within the time frame.
- 30 August 2026
- Organize a webinar to publicize the progress made by the Group and the recommendations in terms of implementing the SEEA from an LNOB perspective.Concept note for webinar.
- List of emails of recipients/invitees.
- Liaison with potential speakers.
- Power points for presentation in the webinar.
- 20 February 2027 11 Support the organization of side events, presentations or other contributions during global and regional meetings to disseminate the work of the Group and the recommendations included in the report.Concept note for at least one event.
- Power point presentation for at least one regional or global meeting to disseminate the work of the Group.
- Facilitating presentation in at least one event, if needed.
- 30 October 2026
- Prepare a short paper summarizing the methodology and recommendations for presentation and sharing in global and regional fora.Abstract for paper, with UN Women s comments included.
- Feedback from Group members incorporated.
- 15 June 2026Draft paper, with UN Women s comments included.
- Feedback from Group members incorporated.
- 15 July 2026
- Consultant s Workplace and Official Travel.
- This is a Bangkok-based consultancy, with the consultant working off-site but may be required to come to UN Women s offices for meetings and discussion. In the event of necessary travel on mission, travel costs and Daily Subsistence Allowance (DSA) will be provided. Travel Authorization will be granted to the consultant prior to the travel date.
- If the selected candidate is not based in the duty station, travel cost to the duty station will be covered and travel will be managed following UN Women travel policy.
- Integrity;.
- Professionalism;.
- Respect for Diversity.
- Awareness and Sensitivity Regarding Gender Issues;.
- Creative Problem Solving;.
- Effective Communication;.
- Inclusive Collaboration;.
- Stakeholder Engagement;.
- Leading by Example.
- Master s Degree in statistics, mathematics, economics, environmental sciences, social sciences, demography, development studies or related fields.
- A first-level university degree in combination with two additional years of qualifying experience may be accepted in lieu of the advanced university degree.
- A minimum 7 years of work experience on calculating environmental and economic statistics, with at least 5 years of experience working with the System of Environmental-Economic Accounts.
- Experience analyzing, producing, or reprocessing micro data in coordination with national government institutions, is required.
- Experience in guiding and contributing to expert groups and working groups with government representatives is required.
- Familiarity with inequity and gender frameworks is desirable.
- Experience in economic or environment statistics in the Asia-Pacific region, as well as at the global level, is desirable.
- Fluency in English is required.
- Exceptional communication, diplomacy and writing skills in English are required.
- A cover letter (maximum length: 1 page).
- 1 or 2 samples of materials (e.g. reports, presentations, papers) led by the applicant, showcasing data on environment-economic statistics, preferably on SEEA related topics, will be requested from shortlisted candidates.
- In July 2010, the United Nations General Assembly created UN Women, the United Nations Entity for Gender Equality and the Empowerment of Women. The creation of UN Women came about as part of the UN reform agenda, bringing together resources and mandates for greater impact. It merges and builds on the important work of four previously distinct parts of the UN system (DAW, OSAGI, INSTRAW and UNIFEM), which focused exclusively on gender equality and women's empowerment.
- At UN Women, we are committed to creating a diverse and inclusive environment of mutual respect. UN Women recruits, employs, trains, compensates, and promotes regardless of race, religion, color, sex, gender identity, sexual orientation, age, ability, national origin, or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, competence, integrity and organizational need.
- If you need any reasonable accommodation to support your participation in the recruitment and selection process, please include this information in your application.
- UN Women has a zero-tolerance policy on conduct that is incompatible with the aims and objectives of the United Nations and UN Women, including sexual exploitation and abuse, sexual harassment, abuse of authority and discrimination. All selected candidates will be expected to adhere to UN Women s policies and procedures and the standards of conduct expected of UN Women personnel and will therefore undergo rigorous reference and background checks. (Background checks will include the verification of academic credential(s) and employment history. Selected candidates may be required to provide additional information to conduct a background check.).
- Note: Applicants must ensure that all sections of the application form, including the sections on education and employment history, are completed. If all sections are not completed the application may be disqualified from the recruitment and selection process.
āļāļąāļāļĐāļ°:
Production planning, Chemical Engineering, Petrochemical
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Monitor, analyze, and optimize production processes to achieve targets in quality, safety, cost efficiency, and operational performance, including troubleshooting process issues, recommending corrective actions, supporting process control improvement, and participating in process safety reviews (HAZOP).
- Coordinate with Production, Maintenance, and related teams to support efficient plant operations and production planning, including developing and improving operating procedures, monitoring equipment performance, and supporting plant startup and shutdo ...
- Analyze process data and support process engineering activities to improve production efficiency, process control, and technology development, including providing technical consultation and troubleshooting support.
- Control and monitor the quality of raw materials, in-process materials, and finished products, including establishing quality specifications, inspection plans, vendor evaluation, and supporting product quality management in compliance with company standards.
- Analyze production data, prepare operational performance reports, and support continuous improvement initiatives through root cause analysis (RCA), benchmarking, and process optimization to reduce costs and improve production efficiency.
- Provide knowledge sharing and training on production processes, safety, and related technologies, while supporting compliance with safety, environmental, energy, company, and regulatory requirements..
- Bachelor s or Master s degree in Chemical Engineering or related field.
- Experience in petrochemical / Ethanol production plant.
- Knowledge of process engineering, data analysis, and process control (APC), including process simulation tools.
- Understanding of process safety (HAZOP) and structured problem-solving methodologies (RCA, Six Sigma, Lean).
- Strong communication and problem-solving skills.
- Able to work effectively with teams and cross-functional teams.
- Continuous improvement mindset with ability to analyze data and enhance production efficiency.
- Work schedule: 6 days/week.
- Locations: Nampong, Khon Kaen / Bo Phloi, Kanchanaburi / Phanom Sarakham, Chachoengsao (Assigned to one of the company sites)..
āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ:
3 āļāļĩāļāļķāđāļāđāļ
āļāļąāļāļĐāļ°:
Power BI, System Testing, SQL, English
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Gather and translate Commercial requirements into analytical and reporting solutions.
- Collaborate with IT and Business teams for issue resolution and enhancement.
- Design, develop, and maintain interactive dashboards and reports using Power BI.
- Work with Global IT team to deploy the requirements.
- Ensure data accuracy, consistency, and governance standards.
- Automate reports and improve reporting efficiency.
- Monitor system performance and ensure system availability.
- Support system testing, upgrades, and deployments.
- Troubleshoot system issues and resolve incidents within defined SLAs.
- Provide first and second level support for business applications and systems.
- Support users with Power BI adoption, training, and best practices.
- Manage end-to-end PepsiConnect data and platform operations.
- Produce daily, weekly, and monthly reports to support GTM execution and performance tracking.
- Provide insights, trends, and recommendations to stakeholders.
- Qualifications:Bachelor s degree in Information Systems, Computer Science, Data Analytics, or related field.
- 3-5 years of experience in FMCG industry.
- Good Communication in Thai and English.
- Strong knowledge of Power BI (DAX, Power Query, data modeling).
- Experience with SQL and relational databases.
- Strong analytical and problem solving skills.
- Strong stakeholder leadership and influence to proactively engage Commercial, Field, and IT teams, align priorities, and translate business needs into scalable analytical and system solutions.
- Prioritization and adaptability to balance operational support with growing analytics and reporting demands.
- Collaboration skills to work effectively with Commercial, Field, IT, and Data teams.
āļāļąāļāļĐāļ°:
ETL, Assurance, Automation
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Analyze business, risk, and regulatory requirements and translate them into system architecture, data models, and ETL designs.
- Design and implement data integration solutions to support Reporting platforms - Perform and oversee data transformation, validation, reconciliation, and data quality assurance to ensure accuracy and regulatory compliance.
- Design, develop, and maintain automation and monitoring scripts using Shell scripts and Batch scripts - Develop, maintain, and optimize PL/SQL objects on Oracle databas ...
- Provide L2/L3 application support, including production issue troubleshooting, impact analysis. - Coordinate with business users, risk teams, and tech teams to resolve data and system issues.
- Oversee and support production operations, batch processing cycles, and critical scheduled jobs.
- Prepare, review, and maintain technical doc (ETL design, data mapping, runbook).
- Ensure full compliance with SDLC, Security policies, Risk controls, and Regulatory standards.
- Apply now if you have these advantages.
- Bachelor s degree or higher in Computer Science / Management Information System or any related field.
- Basic ability to understand business and regulatory requirements with support from senior team members, and assist in translating them into system or data solution requirements.
- Strong ETL frameworks, data lineage, data reconciliation, and data quality management.
- PL/SQL and SQL performance tuning, including query optimization, execution plan analysis, and database efficiency improvement.
- Basic Shell scripting (Bash/KornShell) on Unix/Linux platforms and Windows Batch scripting for automation and operational control.
- Basic Unix/Linux operating systems, including system processes, job scheduling, and troubleshooting.
- Risk Authority, risk management systems, or regulatory reporting applications (Preferable).
- IBM InfoSphere DataStage for enterprise-level ETL and data integration solutions (Preferable).
- Why join Krungsri?.
- As a part of MUFG (Mitsubishi UFJ Financial Group), we a truly a global bank with networks all over the world.
- We offer a striking work-life balance culture with hybrid work policies (3 days in office per week).
- Unbelievable benefits such as attractive bonuses, employee loan with special rates and many more.
- Apply now before this role is close. **.
- FB: Krungsri Career(http://bit.ly/FacebookKrungsriCareer [link removed]).
- LINE: Krungsri Career (http://bit.ly/LineKrungsriCareer [link removed]).
- Talent Acquisition Department
- Bank of Ayudhya Public Company Limited
- 1222 Rama III Rd., Bangpongpang, Yannawa, Bangkok 10120
- āļŠāļāļāļāļēāļĄāļāđāļāļĄāļđāļĨāđāļāļīāđāļĄāđāļāļīāļĄ: Talent Acquisition Center 0-2-----000.
- āļŦāļĄāļēāļĒāđāļŦāļāļļ āļāļāļēāļāļēāļĢāļĄāļĩāļāļ§āļēāļĄāļāļģāđāļāđāļāđāļĨāļ°āļāļ°āļĄāļĩāļāļąāđāļāļāļāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāđāļāļĩāđāļĒāļ§āļāļąāļāļāļĢāļ°āļ§āļąāļāļīāļāļēāļāļāļēāļāļĢāļĢāļĄāļāļāļāļāļđāđāļŠāļĄāļąāļāļĢ āļāđāļāļāļāļĩāđāļāļđāđāļŠāļĄāļąāļāļĢāļāļ°āđāļāđāļĢāļąāļāļāļēāļĢāļāļīāļāļēāļĢāļāļēāđāļāđāļēāļĢāđāļ§āļĄāļāļēāļāļāļąāļāļāļāļēāļāļēāļĢāļāļĢāļļāļāļĻāļĢāļĩāļŊ.
- Remark: The bank needs to and will have a process for verifying personal information related to the criminal history of applicants before they are considered for employment with the bank.
- Applicants can read the Personal Data Protection Announcement of the Bank's Human Resources Function by typing the link from the image that stated below.
- EN (https://krungsri.com/b/privacynoticeen).
- āļāļđāđāļŠāļĄāļąāļāļĢāļŠāļēāļĄāļēāļĢāļāļāđāļēāļāļāļĢāļ°āļāļēāļĻāļāļēāļĢāļāļļāđāļĄāļāļĢāļāļāļāđāļāļĄāļđāļĨāļŠāđāļ§āļāļāļļāļāļāļĨāļŠāđāļ§āļāļāļēāļāļāļĢāļąāļāļĒāļēāļāļĢāļāļļāļāļāļĨāļāļāļāļāļāļēāļāļēāļĢāđāļāđāđāļāļĒāļāļēāļĢāļāļīāļĄāļāđāļĨāļīāļāļāđāļāļēāļāļĢāļđāļāļ āļēāļāļāļĩāđāļāļĢāļēāļāļāļāđāļēāļāļĨāđāļēāļ.
- āļ āļēāļĐāļēāđāļāļĒ (https://krungsri.com/b/privacynoticeth).
āļāļąāļāļĐāļ°:
SQL, Oracle, Data Warehousing
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- Bachelor s degree in computer science, Information Systems, Engineering, or a related field.
- At least 3 years of experience as a Data Engineer or in a related role.
- Hands-on experience with SQL, database management (e.g., Oracle, SQL Server, PostgreSQL), and data warehousing concepts.
- Experience with ETL/ELT tools such as Talend, Apache NiFi, or similar.
- Proficiency in programming languages like Python, Java, or Scala for data manipulation and automation.
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Knowledge of big data technologies such as Hadoop, Spark, or Kafka.
- Strong understanding of data governance, security, and privacy frameworks in a financial services context.
- Excellent problem-solving skills and attention to detail.
- Banking or financial services industry experience, especially in retail or wholesale banking data solutions.
- Certification in cloud platforms (e.g., AWS Certified Data Engineer, Microsoft Azure Data Engineer, Google Professional Data Engineer).
- Contact: K.Chalida 08-------993.
- You have read and reviewed Krung Thai Bank Public Company Limited's Privacy Policy at https://krungthai.com/th/content/privacy-policy. The Bank does not intend or require the processing of any sensitive personal data, including information related to religion and/or blood type, which may appear on copy of your identification card. Therefore, please refrain from uploading any documents, including copy(ies) of your identification card, or providing sensitive personal data or any other information that is unrelated or unnecessary for the purpose of applying for a position on the website. Additionally, please ensure that you have removed any sensitive personal data (if any) from your resume and other documents before uploading them to the website.
- The Bank is required to collect your criminal record information to assess employment eligibility, verify qualifications, or evaluate suitability for certain positions. Your consent to the collection, use, or disclosure of your criminal record information is necessary for entering into an agreement and being considered for the aforementioned purposes. If you do not consent to the collection, use, or disclosure of your criminal record information, or if you later withdraw such consent, the Bank may be unable to proceed with the stated purposes, potentially resulting in the loss of your employment opportunity with.
āļāļĢāļ°āđāļ āļāļāļēāļ:
āļāļēāļāļāļĢāļ°āļāļģ
āđāļāļīāļāđāļāļ·āļāļ:
āļŠāļēāļĄāļēāļĢāļāļāđāļāļĢāļāļāđāļāđ
- āļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āļāļĢāļ°āđāļĄāļīāļāļāļĨāļāļĢāļ°āļāļāļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄāļāļēāļāļāļīāļāļāļĢāļĢāļĄāđāļāļāļļāļāļŠāļēāļŦāļāļĢāļĢāļĄāļāļĨāļąāļāļāļēāļ āļāđāļģāļĄāļąāļāđāļĨāļ°āļāđāļēāļ āļŦāļĢāļ·āļāļāļīāđāļāļĢāđāļāļĄāļĩ.
- āļ§āļēāļāđāļāļāđāļĨāļ°āļāļģāđāļāļīāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāļāļļāļāļ āļēāļāļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄ āđāļāđāļ āļāļļāļāļ āļēāļāļāđāļģ āļāļēāļāļēāļĻ āđāļĨāļ°āļāļīāļ.
- āļāļąāļāļāļēāđāļĨāļ°āļāļģāļĄāļēāļāļĢāļāļēāļĢāļāđāļāļāļāļąāļāđāļĨāļ°āļĨāļāļāļĨāļāļĢāļ°āļāļāļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄāļĄāļēāđāļāđ.
- āļāļĢāļ°āļŠāļēāļāļāļēāļāļāļąāļāļāļĩāļĄāļāļēāļāđāļĨāļ°āļŦāļāđāļ§āļĒāļāļēāļāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāđāļāļ·āđāļāđāļŦāđāļĄāļąāđāļāđāļāļ§āđāļēāļāļēāļĢāļāļāļīāļāļąāļāļīāļāļēāļāđāļāđāļāđāļāļāļēāļĄāļĄāļēāļāļĢāļāļēāļāđāļĨāļ°āļāđāļāļāļģāļŦāļāļāļāļēāļāļāļāļŦāļĄāļēāļĒ.
- āļāļąāļāļāļģāļĢāļēāļĒāļāļēāļāđāļĨāļ°āđāļāļāļŠāļēāļĢāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļāļāļąāļāļāļēāļāļāđāļēāļāļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄ āđāļāđāļ āļĢāļēāļĒāļāļēāļāļāļēāļĢāļāļĢāļ§āļāļŠāļāļāđāļĨāļ°āļĢāļēāļĒāļāļēāļāļāļēāļĢāļāļĢāļ°āđāļĄāļīāļāļāļĨāļāļĢāļ°āļāļ.
- āļŠāļāļąāļāļŠāļāļļāļāļāļēāļĢāļāļąāļāļāļēāļāđāļĒāļāļēāļĒāđāļĨāļ°āļāļĨāļĒāļļāļāļāđāļāđāļēāļāļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄāļāļāļāļāļāļāđāļāļĢ.
- āļāļĢāļīāļāļāļēāļāļĢāļĩāļŦāļĢāļ·āļāļŠāļđāļāļāļ§āđāļēāđāļāļŠāļēāļāļēāļ§āļīāļĻāļ§āļāļĢāļĢāļĄāļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄ.
- āđāļāļĢāļāđāļāļĨāļĩāđāļĒāļŠāļ°āļŠāļĄāđāļĄāđāļāđāļāļĒāļāļ§āđāļē 2.8 āļāļ°āđāļāļ TOEIC āđāļĄāđāļāđāļģāļāļ§āđāļē 650 āļāļ°āđāļāļ āļŦāļĢāļ·āļāđāļāļĩāļĒāļāđāļāđāļē.
- āļāļĢāļ°āļŠāļāļāļēāļĢāļāđ 0-5 āļāļĩāđāļāļāļļāļāļŠāļēāļŦāļāļĢāļĢāļĄāļāļĨāļąāļāļāļēāļ āļāđāļģāļĄāļąāļāđāļĨāļ°āļāđāļēāļ āļŦāļĢāļ·āļāļāļļāļāļŠāļēāļŦāļāļĢāļĢāļĄāļāļīāđāļāļĢāđāļāļĄāļĩ.
- āļĄāļĩāļāļ§āļēāļĄāļĢāļđāđāđāļāļĩāđāļĒāļ§āļāļąāļāļĄāļēāļāļĢāļāļēāļāđāļĨāļ°āļāļāļŦāļĄāļēāļĒāļŠāļīāđāļāđāļ§āļāļĨāđāļāļĄāđāļāļāļĢāļ°āđāļāļĻāđāļāļĒ.
- āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļŠāļ·āđāļāļŠāļēāļĢāļāļĩāđāļĒāļāļāđāļĒāļĩāđāļĒāļĄāļāļąāđāļāđāļāļ āļēāļĐāļēāđāļāļĒāđāļĨāļ°āļ āļēāļĐāļēāļāļąāļāļāļĪāļĐ.
- āļŠāļēāļĄāļēāļĢāļāļāļģāļāļēāļāđāļāđāļāļāļĩāļĄāđāļĨāļ°āļĄāļĩāļāļąāļāļĐāļ°āļāļēāļĢāļāļĢāļīāļŦāļēāļĢāļāļ§āļēāļĄāļŠāļąāļĄāļāļąāļāļāđ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļ§āļīāđāļāļĢāļēāļ°āļŦāđāđāļĨāļ°āđāļāđāđāļāļāļąāļāļŦāļē āļĢāļ§āļĄāļāļķāļāļāļēāļĢāļāļąāļāļŠāļīāļāđāļāļāļĒāđāļēāļāđāļāđāļāļĢāļ°āļāļ.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāđāļāđāđāļāļĢāđāļāļĢāļĄāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāļāļĩāđāđāļāļĩāđāļĒāļ§āļāđāļāļ āđāļāđāļ Microsoft Office; Word, Excel, PowerPoint, Canva.
- āļĄāļĩāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāđāļĢāļĩāļĒāļāļĢāļđāđāđāļĨāļ°āļāļąāļāļāļēāļāļāđāļāļāļāļĒāđāļēāļāļāđāļāđāļāļ·āđāļāļ āļĢāļ§āļĄāļāļķāļāļāļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļāđāļāļāļēāļĢāļāļģāļāļēāļāđāļāļŠāļāļēāļāļāļēāļĢāļāđāļāļĩāđāļāđāļēāļāļēāļĒ5.
āļ§āļīāļĻāļ§āļāļĢāļĢāļ°āļāļ - āļĢāļēāļĒāļĨāļ°āđāļāļĩāļĒāļāļāļēāļāļāļąāđāļ§āđāļāđāļĨāļ°āļŦāļāđāļēāļāļĩāđ
āļ āļēāļāļĢāļ§āļĄ:āļ§āļīāļĻāļ§āļāļĢāļĢāļ°āļāļāļĄāļĩāļŦāļāđāļēāļāļĩāđāļĢāļąāļāļāļīāļāļāļāļāđāļāļāļēāļĢāļāļāļāđāļāļ āļāļīāļāļāļąāđāļ āđāļĨāļ°āļāļģāļĢāļļāļāļĢāļąāļāļĐāļēāļĢāļ°āļāļāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāđāļĨāļ°āđāļāļĢāļ·āļāļāđāļēāļĒ āļāļ§āļāđāļāļēāļĄāļĩāļŦāļāđāļēāļāļĩāđāļĢāļąāļāļāļīāļāļāļāļāđāļāļāļēāļĢāļāļģāđāļŦāđāļĄāļąāđāļāđāļāļ§āđāļēāļĢāļ°āļāļāļĄāļĩāļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒ āđāļāļ·āđāļāļāļ·āļāđāļāđ āđāļĨāļ°āļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ āļ§āļīāļĻāļ§āļāļĢāļĢāļ°āļāļāļāđāļāļāļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāđāļāđāļāļāļĒāđāļēāļāļāļĩāđāļāļĩāđāļĒāļ§āļāļąāļāļŪāļēāļĢāđāļāđāļ§āļĢāđ āļāļāļāļāđāđāļ§āļĢāđ āđāļĨāļ°āļĢāļ°āļāļāđāļāļĢāļ·āļāļāđāļēāļĒāļāļāļāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ
āļāļ§āļēāļĄāļĢāļąāļāļāļīāļāļāļāļāļĢāđāļ§āļĄāļāļąāļ:
āļāļēāļĢāļāļāļāđāļāļāļĢāļ°āļāļ:
āļ§āļīāļĻāļ§āļāļĢāļĢāļ°āļāļāļĄāļĩāļŦāļāđāļēāļāļĩāđāļāļāļāđāļāļāđāļĨāļ°āļ§āļēāļāļĢāļ°āļāļāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāđāļĨāļ°āđāļāļĢāļ·āļāļāđāļēāļĒ āļāđāļāļāļĄāļĩāļāļ§āļēāļĄāđāļāđāļēāđāļāļāļĒāđāļēāļāļāđāļāļāđāļāđāđāļāļĩāđāļĒāļ§āļāļąāļāļŪāļēāļĢāđāļāđāļ§āļĢāđ āļāļāļāļāđāđāļ§āļĢāđ āđāļĨāļ°āļĢāļ°āļāļāđāļāļĢāļ·āļāļāđāļēāļĒāļāļāļāļāļāļĄāļāļīāļ§āđāļāļāļĢāđ
āļāļēāļĢāđāļāđāđāļāļāļąāļāļŦāļē:
āļ§āļīāļĻāļ§āļāļĢāļĢāļ°āļāļāļāđāļāļāļŠāļēāļĄāļēāļĢāļāđāļāđāđāļāđāļĨāļ°āđāļāđāđāļāļāļąāļāļŦāļēāđāļāđ āļāļĩāđāđāļāļīāļāļāļķāđāļāļāļąāļāļĢāļ°āļāļāļāļāļĄāļāļīāļ§āđāļāļāļĢāđāđāļĨāļ°āđāļāļĢāļ·āļāļāđāļēāļĒāđāļāđ
āļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒ:
āļ§āļīāļĻāļ§āļāļĢāļĢāļ°āļāļāļāđāļāļāļĄāļąāđāļāđāļāļ§āđāļēāļĢāļ°āļāļāļĄāļĩāļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒāđāļĨāļ°āļāļēāļĢāļĨāļ°āđāļĄāļīāļāļāļ§āļēāļĄāļāļĨāļāļāļ āļąāļĒāđāļāđ āđāļāđāļĢāļąāļāļāļēāļĢāđāļāđāđāļāļāļĒāđāļēāļāļĢāļ§āļāđāļĢāđāļ§āđāļĨāļ°āļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļ
āļāļēāļĢāļāļģāļĢāļļāļāļĢāļąāļāļĐāļē:
āļ§āļīāļĻāļ§āļāļĢāļĢāļ°āļāļāļāđāļāļāļāļģāļĢāļļāļāļĢāļąāļāļĐāļēāļĢāļ°āļāļāđāļĨāļ°āđāļāļĢāļ·āļāļāđāļēāļĒāđāļāļ·āđāļāđāļŦāđāđāļāđāđāļāļ§āđāļēāļāļģāļāļēāļāđāļāđāļāļĒāđāļēāļāļĄāļĩāļāļĢāļ°āļŠāļīāļāļāļīāļ āļēāļāđāļĨāļ°āļāļĨāļāļāļ āļąāļĒ
āļāļēāļĢāļāļąāļāļāļģāđāļāļāļŠāļēāļĢ:
āļ§āļīāļĻāļ§āļāļĢāļĢāļ°āļāļāļāđāļāļāļāļąāļāļāļķāļāļāļēāļĢāđāļāļĨāļĩāđāļĒāļāđāļāļĨāļāđāļĨāļ°āļāļēāļĢāļāļĢāļąāļāļāļĢāļļāļāļāļąāđāļāļŦāļĄāļāļāļāļāļĢāļ°āļāļāđāļĨāļ°āđāļāļĢāļ·āļāļāđāļēāļĒ
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āļĒāļāļāļāļīāļĒāļĄ
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