Entry - Level Data Engineer at GM, focusing on building large scale data platforms in cloud environments. Collaborating with data engineers and scientists while migrating systems to cloud solutions.
About the role
- Data Engineer responsible for developing ETL pipelines and ensuring data solutions in AWS data lakes/warehouses. Collaborating with teams and focusing on data quality, compliance, and performance optimization.
Responsibilities
- Design, develop, and maintain ETL pipelines using AWS Glue, Glue Studio, and Glue Catalog.
- Ingest, transform, and load large datasets from structured and unstructured sources into AWS data lakes/warehouses.
- Work with S3, Redshift, Athena, Lambda, and Step Functions for data storage, query, and orchestration.
- Build and optimize PySpark/Scala scripts within AWS Glue for complex transformations.
- Implement data quality checks, lineage, and monitoring across pipelines.
- Collaborate with business analysts, data scientists, and product teams to deliver reliable data solutions.
- Ensure compliance with data security, governance, and regulatory requirements (BFSI preferred).
- Troubleshoot production issues and optimize pipeline performance.
Requirements
- 9+ years of experience in Data Engineering, with at least 5+ years on AWS cloud data services.
- Strong expertise in AWS Glue, S3, Redshift, Athena, Lambda, Step Functions, CloudWatch.
- Proficiency in PySpark, Python, SQL for ETL and data transformations.
- Experience in data modeling (star, snowflake, dimensional models) and performance tuning.
- Hands-on experience with data lake/data warehouse architecture and implementation.
- Strong problem-solving skills and ability to work in Agile/Scrum environments.
- AWS Certified Data Analytics – Specialty or AWS Solutions Architect certification.
- Familiarity with CI/CD pipelines for data engineering (CodePipeline, Jenkins, GitHub Actions).
- Knowledge of BI/Visualization tools like Tableau, Power BI, QuickSight.
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