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Tier-1 brand and metro location increase applicant density, while seniority and niche skills moderate it.
Enterprise-scale data engineering plus regulated-industry compliance (HIPAA, GxP) demands specific domain experience, limiting transferability.
Explicit 15+ years requirement plus mandatory Snowflake/DBT/Iceberg and regulatory experience makes filtering strict.
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Define and execute enterprise-wide data engineering strategy aligned with 2030 Data Strategy, delivering measurable business value through capability models and adoption roadmaps.
Build, lead, and scale a high-performing team to manage end-to-end data engineering capabilities including acquisition, storage, ingestion, transformation, orchestration, CI/CD, using Snowflake ecosystem tools and ensure compliance with regulatory requirements.
Govern and automate data engineering solutions with a focus on standardization, quality controls, cost efficiency, performance SLAs/SLOs, and partner cross-functionally to enable adoption and operational excellence at scale.
15+ years in data engineering leadership roles at an enterprise scale.
Hands-on experience with Snowflake, Fivetran, DBT, DataOps.live, SnowPark Container Services, Apache Iceberg, Snowflake Horizon, Snowflake Cortex, and AWS S3 mandatory.
Experience working in regulated industries with data privacy and compliance requirements including HIPAA, GxP, and SOx.
Work Experience Required: 15+ years in data engineering leadership (enterprise capacity).
Proven leadership in building and scaling global federated data engineering teams and capabilities from the ground up.
Strategic operator skilled in defining enterprise service tiers, maturity models, and adoption programs to drive standardization and automation.
Experience collaborating across matrixed environments with technical and business stakeholders to deliver regulated and compliant data solutions at scale.