AVP - Data Engineering

Key Responsibilities

• Define and execute the enterprise data strategy and architecture roadmap for SK Finance.
• Build, scale, and operate the enterprise data platform on Azure Databricks Delta Lake, PySpark, and SQL at the core.
• Engineer scalable, observable ETL and ELT pipelines across batch and real-time workloads, with documented quality and SLA discipline.
• Enable downstream AI, ML, and analytics use cases through production-grade data pipelines and feature engineering foundations.
• Own data governance, data quality, and platform security through Unity Catalog, with full compliance posture against DPDP, RBI, and internal audit expectations.
• Partner with business, credit, risk, and analytics teams to convert demand into outcome-anchored data products.
• Lead, mentor, and grow the data engineering team into a senior-bench function with structured succession.

Key Requirements

• 1215 years in data engineering, analytics platforms, or big data engineering at enterprise scale.
• Mandatory hands-on expertise in Azure and Databricks Lakehouse architecture, Delta Lake, Unity Catalog.
• Deep proficiency in Spark, Python, SQL, and modern ETL/ELT frameworks.
• Working experience across cloud platforms (Azure primary, AWS strongly preferred).
• Demonstrated exposure to AI/ML pipelines, feature stores, and MLOps disciplines.
• Domain experience in NBFC, banking, or financial services strongly preferred regulatory awareness on data and DPDP an advantage.