AWS Data Engineer

Responsibilities:

• Designed and developed scalable AWS Data Lake solutions for ingesting, processing, and analyzing enterprise data.
• Built and maintained ETL/ELT pipelines using AWS Glue , PySpark, and AWS Lambda.
• Developed batch and real time data pipelines integrating multiple source systems into Amazon S3 data lakes.
• Implemented data transformation, cleansing, and enrichment processes using AWS Glue Jobs and Glue Workflows.
• Managed metadata catalogs using AWS Glue Data Catalog and optimized data discovery capabilities.
• Designed data ingestion frameworks leveraging S3, Lambda, EventBridge, and Step Functions.
• Created analytical datasets for reporting and business intelligence consumption.
• Optimized Glue jobs for performance, scalability, and cost efficiency.
• Implemented data quality, governance, and monitoring controls across pipelines.
• Worked with Redshift, Athena, and Lake Formation to support data analytics and governance initiatives.
• Developed CI/CD processes for data pipelines and automated deployment across environments.
• Collaborated with business stakeholders, application teams, and data architects to define data integration requirements.

Required Skills:

• AWS Glue
• AWS Data Lake
• Amazon S3
• PySpark, Python
• Athena, Redshift
• Lambda, Step Functions
• SQL & Data Modeling
• ETL/ELT Development
• Lake Formation
• Data Governance & Data Quality
• Event Bridge and Data Pipeline Automation