Description

You will lead the design and implementation of scalable data architectures and processing pipelines.

Responsibilities

• Design and implement scalable ETL/ELT pipelines using AWS Glue, Spark (PySpark), and Step Functions.
• Build and maintain data lakes and data warehouses using S3, Aurora, and Athena.
• Develop serverless data processing solutions using AWS Lambda and integrate them into orchestration workflows.
• Implement data governance, security, and access control using Lake Formation, IAM, and CloudFront.
• Perform advanced SQL and PL/SQL development for data transformation, analysis, and performance tuning.
• Optimize data pipelines for reliability and monitor performance across the stack.

Required Skills

• 5+ years of experience in data engineering.
• Proficiency in Python, PySpark, and SQL (basic and advanced) including PL/SQL.
• Extensive experience with AWS data services: Glue, Athena, Step Functions, Lambda, Lake Formation, S3, EC2, Aurora, EBS/EFS, and CloudFront.
• Hands-on experience with S3, Athena, SNS, SQS, CDK, and GitHub.
• Strong understanding of ETL/ELT processes and data warehousing concepts.
• Experience in data modeling (conceptual, logical, physical) for OLTP and OLAP environments.
• Ability to manage structured and semi-structured data using Athena and S3.
• Proven debugging and performance tuning skills across distributed data processing systems.

Key Skills

Education

Any Gradute