AVP - Data Engineering
SK Finance
- Location
- Mumbai, Maharashtra, India
- Job type
- Full-time
Required skills
- Python
- AWS
- Azure
- banking
- compliance
- data strategy
- Databricks
- ETL
- SQL
- Unity
About the role
SK Finance
Website:
skfin.in
Job details:
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
- 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, 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.
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