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 — Data 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 12–15 years in data engineering, analytics platforms, or big data engineering at enterprise scale. Mandatory hands-on expertise in Azure and Databricks — Lakehouse architecture, Data 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.