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Popular data role, broad AWS/PySpark skillset and metro locations increase applicant competition.
Core data engineering skills (AWS, PySpark, SQL) are broadly transferable across industries.
Strong mandatory AWS, PySpark and data platform skill requirements increase technical screening rigor.
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Develop, maintain, and migrate data pipelines and products on a modern AWS cloud data platform for multiple business units.
Design and implement data processing environments and integrations using AWS PaaS including Glue, EMR, S3, Lambda, Step Functions, Redshift, and Sagemaker.
Contribute to improving enterprise-wide data engineering capabilities and create reusable architecture patterns aligned with data mesh governance and data integrity standards.
Strong experience with AWS data architecture and services including S3, Glue, EMR, Sagemaker, Aurora, and Redshift.
Proficiency in SQL, Python, PySpark and data pipeline development including Apache Airflow, CloudFormation, Lambda, and Step Functions.
Hands-on experience with relational databases like Postgres and Redshift, code versioning with Git, and Agile development practices with CI/CD tools.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building and migrating complex data architectures aligned with data mesh organizational models and federated governance.
Technically rigorous with ability to design reusable, cost-effective data solutions operating across full data lifecycles and medallion architectures.
Comfortable working embedded within business units, supporting data product delivery with strong collaboration and communication skills.