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Pune metro and broad requirements increase competition, but seniority and Snowflake specialization moderate applicant density.
Core data engineering skills are transferable, but heavy Snowflake and finance governance focus increases domain specificity.
Explicit 10+ years, mandatory Snowflake/platform leadership, and framework authorship make shortlisting stringent.
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Establish and lead six foundational engineering frameworks for data pipeline lifecycle including DataOps, Data Quality, Observability, Modeling, Governance, and Pipeline Design Pattern library within the Finance Data Hub.
Drive adoption of these frameworks through documentation, enablement sessions, and enforcing conformance criteria to improve production quality and delivery velocity across data teams.
Lead modernization initiatives and integrate GenAI tools to enhance data engineering workflows, focusing on scalable solutions in Snowflake and automated CI/CD pipelines.
B.E./M.Tech in Electrical, Electronics, or Computer Science or related field.
10+ years of experience in end-to-end delivery of enterprise-scale data pipelines with strong SQL and Python proficiency.
Deep hands-on expertise with Snowflake platform including data sharing, RBAC, row-level security, and query optimization.
Experience with GitHub-based CI/CD pipeline automation and designing branching/automated test strategies for data workloads.
Technical leader with experience setting standards and influencing engineering direction across cross-functional teams without direct managerial authority.
Proficient in designing reusable frameworks and enforcing data engineering best practices aligned with modern architectures like medallion and dimensional modeling.
Experienced in driving data engineering modernization and operational efficiency improvements with measurable impact, including leveraging GenAI tools for productivity enhancement.