About the role
Requirements:
- 7+ years of hands-on data engineering experience in cloud-native environments (AWS required).
- 5+ years of strong proficiency in Python and PySpark for data processing, transformation, and pipeline development.
- 7+ years of experience in advanced SQL skills, complex queries, window functions, performance tuning, and query optimization across Redshift and Spark SQL.
- 5+ years of hands-on experience with AWS Glue, EMR, Databricks, or similar Spark-based compute services.
- Experience supporting multi-tenant platforms, understanding tenant isolation, shared frameworks, and config-driven patterns.
- Strong troubleshooting and debugging skills and ability to diagnose and resolve production pipeline failures quickly.
- Excellent communication skills, ability to engage with multiple tenant teams, provide technical consulting, and explain complex solutions clearly
Nice to Have:
- Experience with Airflow or Autosys for workflow orchestration.
- Experience with AWS Lake Formation for governance and fine-grained access control.
- Familiarity with SAP source systems or enterprise ERP data.
- Experience with CloudWatch for monitoring and observability; AWS GovCloud experience.
- Data modeling experience (star schema, data vault).
- Experience with Parquet, Avro, and Iceberg file/table formats.