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Tier-1 brand, mid-level generalist data role, and metro locations increase competition.
Core data engineering skills and cloud experience are highly transferable across industries.
Explicit 4–7 years plus mandatory cloud, Python, data pipeline, and DB skills enforce strict filters.
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Design, develop, and maintain scalable data pipelines and backend data systems across cloud and on-premises platforms supporting analytics, reporting, and Generative AI (GenAI) use cases.
Build and support ETL/ELT workflows using Python, AWS Lambda, AWS Glue, Apache Airflow, and batch or streaming frameworks.
Administer and optimize databases across multiple platforms and implement data quality practices including validation, logging, and anomaly detection.
Bachelor's or Master's degree in Engineering, Technology, or Computer Applications from an accredited university.
4-7 years of experience in data engineering, Python programming, and SQL or PL/SQL database administration.
Experience with AWS Lambda, AWS Glue, Apache Airflow, and batch/streaming data pipeline development.
Experience designing data processing workflows for Generative AI or large language model applications.
Experienced in supporting end-to-end data engineering solutions that facilitate enterprise-level analytics and GenAI workflows.
Proficient in multiple database environments including Oracle, PostgreSQL, MySQL, Microsoft SQL Server, Amazon Redshift, Google BigQuery, and Snowflake, with optimization capabilities.
Familiar with implementing data quality controls and operational reliability measures in production data pipelines across cloud and on-prem environments.