Build and maintain ETL pipelines using Python, PySpark, and AWS Glue, orchestrating workflows with Step Functions and Lambda while optimizing Redshift and S3 storage solutions.
- Mid Level Dev AWS Data Engineer with 4-8 years of software development experience Build and maintain ETL pipelines using Python and PySpark on AWS Glue and related platforms. - Orchestrate workflows using AWS Step Functions and Lambda. - Implement messaging and event-driven integrations using SNS and SQS. - Design and optimize storage and querying solutions in Amazon Redshift, RDS, Oracle and S3-based architectures. - Write efficient SQL for transformations, validation, and reporting. - Integrate data from APIs and process structured and semi-structured JSON data. - Implement data quality checks, monitoring, and operational support processes. - Participate in CI/CD and version control practices for deployment and release management. - Collaborate with cross-functional teams to translate business requirements into technical solutions. - 4-8 years of software development experience across the appropriate platform. - Strong hands-on experience with Python, PySpark, API’s and SQL. - Experience with ETL/data pipeline development and Orchestration using Step functions / AirFlow . - Working knowledge of AWS services including Glue, Lambda, Step Functions, Redshift, S3, SNS, and SQS. - Experience with Athena, EMR, Kinesis, DynamoDB, or RDS. - Good Knowledge on CloudWatch, logging, and production support. - Understanding of data warehousing, data lakes, Lake House and query optimization. - Experience with GitLab/Terraform or similar and CI/CD workflows. - Good understanding of using AI tools like Github Copilot or similar for code productivity - Exposure to enterprise data lake or cloud migration initiatives. - Have an eye to solving complex problems, great communication with stakeholders - Have a good understanding of performance engineering of code pipelines and near real time systems - Good understanding on Agents and MCP"