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Strong employer brand, generalist data title, in-demand SQL/PySpark/Snowflake skills, and GenAI buzz drive high competition.
Core data engineering and BI skills are widely transferable across industries.
Mandatory SQL/PySpark, Snowflake and ETL tool experience enforces moderate technical filtering.
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Own and optimize data quality assurance, data audit, data governance, and related reporting processes to reduce manual work and costs.
Develop and implement Natural Language Query (NLQ) and GenAI/Agentic AI frameworks in data engineering use cases, including leveraging Amazon Bedrock for bank data scenarios.
Work hands-on with SQL, Python/PySpark, data sources like Snowflake and various databases, and ETL tools (Informatica, AWS Glue) to support data mining and business intelligence reporting.
Strong SQL expertise and hands-on experience with Python and PySpark.
Experience with Natural Language Query (NLQ) implementations and GenAI/Agentic AI frameworks in data engineering.
Familiarity with data sources such as Snowflake, relational and non-relational databases.
Experience with ETL tools like Informatica and AWS Glue; Work Experience Required: Not explicitly mentioned in the JD.
Proven ability to enhance data quality and governance processes through automation and AI-driven solutions.
Experience with integrating and operationalizing GenAI use cases in enterprise-level data engineering environments.
Comfortable working in a data-driven role requiring hands-on coding, tooling expertise, and business intelligence reporting.