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Tier-1 brand, metro locations, and broad multi-cloud/vector/ETL skill requirements increase applicant competition.
Core data engineering and cloud skills transfer well, but GenAI/vector DB specialization increases domain sensitivity.
Explicit 7–10 years requirement plus mandatory data engineering, cloud, and GenAI tooling makes filters stringent.
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Design, develop, and maintain scalable data pipelines and backend systems across cloud and on-premises environments supporting analytics, reporting, and GenAI/LLM applications.
Build and support ETL and ELT workflows using Python, AWS Lambda, AWS Glue, Apache Airflow, and batch or streaming frameworks.
Administer and optimize databases (Oracle, PostgreSQL, MySQL, SQL Server, Redshift, BigQuery, Snowflake), troubleshoot issues, and collaborate with cross-functional, onsite, and offshore teams.
Bachelor's or Master's degree in Engineering, Technology, or Computer Applications from accredited university.
7-10 years of experience in data engineering, Python programming, and database administration with SQL or PL/SQL.
Proven experience building data pipelines using AWS Lambda, AWS Glue, Apache Airflow, or similar orchestration frameworks.
Experience designing data processing for GenAI/LLM including data cleaning, embedding workflows, vector store ingestion, or RAG pipeline enablement.
Experienced in end-to-end data engineering for enterprise-grade solutions involving GenAI/LLM use cases and advanced analytics.
Skilled at working in cross-functional and multi-location teams including mentoring juniors and coordinating with onsite/offshore teams.
Strong expertise with multiple relational and cloud-native database platforms and orchestration tools for batch and streaming data workflows.