ประสบการณ์:

6 ปีขึ้นไป

ทักษะ:

Cloud Computing, Architecture, Postgre SQL, Automation, Leadership Skill

ประเภทงาน:

งานประจำ

• AI & Advanced Analytics Enablement.
• Lead the design, development, and deployment of enterprise AI, Machine Learning, and Generative AI solutions to support business transformation (e.g., pricing, promotion, automation, recommendation, demand forecasting, customer insights, and intelligent decision support).
• Build and manage scalable data pipelines supporting AI/ML model training, inference, feature engineering, Feature Store, RAG knowledge bases, and LLM applications.
• Develop reusable datasets, AI-ready data products, feature engineering pipelines, and semantic data models for AI Engineers, Data Scientists, and Business Analytics teams.
• Partner closely with AI Engineers to design, deploy, productionize, and scale AI/ML models, LLM applications, Agentic AI, AI Chatbots, Recommendation Systems, and Mobile AI applications.
• Design and maintain data ingestion pipelines for structured, semi-structured, and unstructured data from enterprise systems, APIs, databases, files, event streams, IoT devices, and third-party platforms.
• Develop scalable data pipelines supporting document ingestion, embedding generation, metadata management, vector indexing, and retrieval workflows for RAG applications.
• Identify opportunities to embed AI into business workflows and operational decision-making to improve efficiency, customer experience, and business value.
• Data Platform & Engineering Leadership.
• Own end-to-end enterprise data architecture from source systems to Data Lake, Lakehouse, Data Warehouse, Feature Store, Semantic Layer, and AI-serving layers.
• Design, develop, and optimize scalable ETL/ELT pipelines supporting batch, micro-batch, streaming, and near real-time data processing.
• Design and maintain workflow orchestration for enterprise data pipelines using Databricks Workflows, Apache Airflow, or equivalent orchestration frameworks.
• Develop enterprise-scale Big Data solutions using Apache Spark and distributed computing frameworks.
• Design scalable logical and physical data models while optimizing database architecture for performance, scalability, reliability, and cost efficiency.
• Ensure enterprise data quality, governance, lineage, metadata management, observability, security, and compliance across data platforms.
• Implement automated data validation, monitoring, logging, alerting, and observability to ensure production-grade data reliability.
• Optimize SQL queries, Spark workloads, partitioning strategies, storage formats, and compute resources for maximum performance and cost efficiency.
• Analyze complex technical issues, identify root causes, troubleshoot production problems, and recommend infrastructure and platform improvements.
• Select, evaluate, and integrate modern data engineering tools, cloud technologies, and AI platform frameworks to support evolving business needs.
• Continuously evaluate emerging technologies in Big Data, Lakehouse Architecture, Data Platform Engineering, Cloud Computing, and AI Platform Engineering.
• AI Platform & Infrastructure.
• Build and maintain enterprise AI data infrastructure supporting LLM, RAG, Agentic AI, AI Chatbots, Recommendation Engines, Intelligent Search, and Intelligent Automation platforms.
• Design and implement scalable AI data pipelines supporting batch, streaming, vector search, embedding pipelines, and Retrieval-Augmented Generation (RAG) architectures.
• Design scalable APIs, data services, and integration layers connecting AI applications with enterprise systems and digital platforms.
• Collaborate with AI Engineers to prepare high-quality datasets for LLM fine-tuning, prompt engineering, model evaluation, inference, and continuous model improvement.
• Support deployment and operationalization of AI products using DataOps, MLOps, CI/CD, containerization, and modern software engineering practices.
• Drive continuous improvements in platform scalability, availability, security, observability, resilience, and operational efficiency.
• Team Leadership & Capability Building.
• Lead, mentor, and develop a high-performing team of Data Engineers, AI Engineers, and Analytics professionals.
• Foster engineering excellence through best practices in architecture design, coding standards, testing, code reviews, deployment, technical documentation, and software engineering.
• Drive architecture reviews, technical design reviews, engineering governance, and platform standardization across the engineering organization.
• Define engineering standards, technical roadmaps, platform architecture, and technology strategy aligned with business objectives.
• Collaborate closely with Product Owners, Business Stakeholders, AI Engineers, Data Scientists, Solution Architects, Infrastructure, and DevOps teams.
• Provide hands-on technical leadership with a strong engineering mindset and willingness to troubleshoot complex production systems.
• Promote continuous learning, knowledge sharing, innovation, and adoption of emerging technologies across the engineering team.
• Bachelor's degree or higher in Computer Science, Computer Engineering, Information Technology, Artificial Intelligence, Data Engineering, Management Information Systems, or a related field.
• 6+ years of experience in Data Platform Engineering, Data Engineering, Big Data, or AI Platform development, with experience leading engineering teams.
• Strong experience designing and implementing enterprise-scale Data Lake, Lakehouse, Data Warehouse, or Modern Data Platform architectures.
• Expert proficiency in SQL, Databricks SQL, PostgreSQL, database design, and query performance optimization.
• Strong programming skills in Python.
• Hands-on experience with Apache Spark, Databricks, Delta Lake, Spark SQL, Unity Catalog, and distributed data processing.
• Strong experience designing and developing scalable ETL/ELT pipelines and workflow orchestration.
• Experience with streaming technologies such as Spark Structured Streaming, Kafka, or equivalent.
• Experience building enterprise-scale data platforms supporting AI/ML, Advanced Analytics, and Generative AI applications.

ประสบการณ์:

5 ปีขึ้นไป

ทักษะ:

Windows Server, Architecture, Automation, Kubernetes, Python, DevOps, Oracle, Linux

ประเภทงาน:

งานประจำ

• Develop and maintain technical security hardening baselines for all major platforms Windows Server, RHEL/Ubuntu, databases (Oracle, MSSQL, PostgreSQL), and cloud services across GCP, Azure, AWS.
• Translate security architecture decisions into implementable standards documents with clear acceptance criteria, configuration examples, and validation scripts.
• Run proof-of-concept testing for new security controls and technologies, documenting performance impact, integration requirements, and operational considerations.
• Build and maintain automated compliance scanning ensuring hardening baselines are continuously validated against deployed configurations using tools like Tenable, Qualys, or cloud-native benchmarks.
• Conduct technical security configuration reviews for new infrastructure deployments, cloud landing zones, and major application releases before go-live.
• Manage the security standards lifecycle regular reviews, version control, exception management, and sunset processes for deprecated standards.
• Partner with infrastructure and DevOps teams to embed security baselines into golden images, Terraform modules, and CI/CD templates.
• Produce technical testing reports for firewall rule reviews, network segmentation validation, and access control configuration assessments.
• Maintain a standards adoption dashboard tracking which teams have implemented which baselines and where gaps exist.
• TECHNICAL REQUIREMENTS.
• Hardening frameworks: CIS Benchmarks (Windows, Linux, cloud), NIST SP 800-123, vendor-specific security guides.
• Scanning and compliance: Tenable Nessus/Tenable.io, Qualys VMDR, cloud-native compliance tools.
• Operating systems: Windows Server 2016/2019/2022, RHEL 8/9, Ubuntu deep configuration knowledge.
• Cloud platforms: GCP, Azure, AWS security configuration and baseline management at the service level.
• Scripting: PowerShell, Bash, Python for compliance validation, automated checks, and reporting.
• IaC familiarity: Terraform, Ansible understanding security integration points.
• MUST-HAVE REQUIREMENTS.
• These are non-negotiable. If you do not meet all of these, this role is not the right fit.
• 5+ years in information security with hands-on experience in infrastructure hardening, security configuration, and technical standards development.
• Deep knowledge of CIS Benchmarks or DISA STIGs you've implemented these on real systems, not just read the PDFs.
• Hands-on experience with at least two: Windows Server hardening, Linux hardening, database security configuration, or cloud security baselines.
• Experience with vulnerability scanning and compliance tools Tenable, Qualys, or cloud-native equivalents (AWS Config, Azure Policy, GCP Security Health Analytics).
• Ability to write clear technical documentation that infrastructure teams can implement without hand-holding.
• Working proficiency in scripting (PowerShell, Bash, or Python) for configuration validation and automation.
• NICE-TO-HAVE.
• Experience with infrastructure-as-code security writing security-hardened Terraform modules or Ansible playbooks.
• Familiarity with container and Kubernetes security hardening (CIS Kubernetes Benchmark, pod security standards).
• CompTIA Security+, CIS Benchmarks certification, or relevant cloud security certifications (AZ-500, GCP Professional Cloud Security Engineer).
• Experience in retail or high-availability environments where security hardening must balance with uptime requirements.