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High candidate density due to strong employer brand, Gurgaon location, and broad, in-demand data engineering skillset.
High because role requires financial investment-data experience and domain-specific data handling practices.
High due to explicit 7-10 years and mandatory Snowflake, AWS, and data pipeline expertise.
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Own maintenance, governance, development of common frameworks and tools for FIL Data Lake focusing on Data Ecosystem for reporting and analytics.
Collaborate with engineering, data teams, and technical data architects to strategize data management and Data Lake adoption.
Integrate siloed data from disparate systems for centralized data access and governance.
7-10 years of relevant work experience.
Strong experience with Data Ingestion, Transformation, and Distribution using AWS or Snowflake.
Hands-on knowledge of SnowSQL, Snowpipe, role-based access controls, Snaplogic ETL/ELT tools, and AWS services (EC2, Lambda, ECS/EKS, DynamoDB, VPCs).
Experience in designing and implementing data pipelines, CI/CD processes for Snowflake, and data lake concepts handling structured, semi-structured, and unstructured data.
Experienced in building scalable data ingestion and orchestration pipelines using Snaplogic, AWS, and Kafka.
Skilled in implementing data quality, testing, and code coverage strategies for large, complex datasets across multiple markets and geographies.
Capable of collaborating across roles and business units to translate strategic data goals into operational systems within a mission-critical financial services environment.