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Tier-1 brand and Bangalore metro increase applicant density but senior niche platform skills moderate competition.
Platform and governance expertise are transferable across industries but finance domain preference raises sensitivity to domain fit.
Explicit 14+ years requirement plus mandated Databricks/Snowflake/Spark/Iceberg and governance skills creates high filter strictness.
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Design, build, and operate modern data platform capabilities including lakehouse, batch/streaming pipelines, metadata, observability, security, and governed consumption.
Develop scalable data pipelines using Apache Spark, Snowflake, Databricks with focus on performance, reliability, cost efficiency, and software engineering best practices.
Lead a small team/pod for delivery oversight, mentoring, incident response, operational stability, and platform reliability improvements.
14+ years in software/data engineering with hands-on coding in Python and SQL (Java/Scala is a plus).
Proven experience delivering lakehouse/data platform solutions using Databricks and/or Snowflake on AWS (GCP/Azure acceptable).
Strong expertise with Spark (batch/streaming), Apache Iceberg table/catalog governance, and production engineering practices including CI/CD, Infrastructure as Code, automated testing, and observability.
Work Experience Required: 14+ years relevant experience; Education: BS/MS in Computer Science/Engineering or equivalent experience.
Experienced in large-scale data platform architecture and modern cloud data solutions focused on investment management domain or financial services preferred.
Operates with strong ownership mindset including operational responsibilities like incident response and root cause analysis across global teams.
Skilled in engineering data governance, data product contracts, and AI-ready data asset creation with a focus on reliability, cost optimization, and governed data consumption.