Job Summary

Design, develop, and optimize enterprise-scale data platforms on Microsoft Azure Cloud.

Build scalable and secure ETL/ELT data pipelines to support Data Lake, analytics, and AI/ML initiatives.

Ensure high-quality, reliable, and cost-efficient data architecture while driving continuous improvement and technical best practices.

Collaborate with cross-functional teams to deliver robust data engineering solutions and mentor junior engineers.

Key Responsibilities

Design and develop scalable data pipelines and cloud-based data architecture using Azure Data Factory (ADF), Databricks, Microsoft Fabric, and SSIS.

Build ETL/ELT processes to integrate data from multiple sources such as SAP, ERP systems, APIs, and Excel into enterprise Data Lake and databases.

Develop and optimize SQL, Python, and PySpark scripts for large-scale data processing.

Monitor daily data pipeline operations, troubleshoot complex technical issues, and perform root cause analysis.

Ensure data quality, accuracy, security, and optimize database performance.

Prepare technical documentation, operation manuals, and share best practices with the team.

Support continuous improvement by adopting emerging technologies and mentoring junior data engineers.

Qualifications

Bachelor's degree in Computer Science, Information Technology, Computer Engineering, or related fields.

Minimum

5 years

of experience in Data Engineering or related roles.

Strong experience with

Microsoft Azure

, Azure Data Factory (ADF), Databricks, Microsoft Fabric, and Data Lake.

Strong proficiency in

SQL Server, T-SQL, Python, and PySpark

Experience in designing Data Warehouse, ETL/ELT processes, and integrating data from ERP/SAP systems and APIs.

Knowledge of Cloud Architecture, CI/CD, DevOps practices, and Database Performance Tuning.

Strong analytical and problem-solving skills with the ability to perform root cause analysis.

Passion for learning new technologies and mentoring team members.