Job Summary To effectively design, develop, and manage data solutions using ETL technologies such as Azure Databricks (ADB) , Azure Data Factory (ADF) and SQL

Responsibilities

- Translate business requirements into technical solutions in collaboration with the PMO team.

- Own end-to-end delivery of data projects, ensuring on-time execution and adherence to quality standards.

- Design technical architecture and guide development efforts for enhancements and new projects.

- Develop and maintain robust ETL pipelines and data integration modules across systems.

- Ensure high data quality, data anomaly resolution of critical process issues.

- Monitor and resolve performance bottlenecks in data workflows and programs.

- Establish best practices, standard operating procedures, and drive their implementation across teams.

- Act as a liaison with business users and product managers to support daily data needs and strategic initiatives.

- Coordinate with internal and external development teams to troubleshoot and resolve issues efficiently.

- Manage workload through effective planning, prioritization, and progress tracking.

Key Decisions / Dimensions

- Define semantic layer design and metric definitions





- Prioritize data vs AI optimization trade-offs

- Handle production issues with RCA and long-term fixes

- Drive architectural decisions for lakehouse + Data integration

Major Challenges

- Ensuring Data Delivery within TAT

- Driving adoption of GenAI-based BI over traditional dashboards

- Balancing performance, cost, and scalability

- Managing dependencies across data engineering, AI, and business teams

Required Qualifications and Experience Must Have

- Azure Databricks - PySpark, SQL, Delta Lake

- Semantic Modeling & Metrics Layer design

- Hands-on with Databricks workflows

- Pyspark (Pandas, PySpark, FastAPI)

- Azure Data Factory (ADF) for ETL pipelines

- Solid SQL and data modeling skills

Good to Have

- Cosmos DB / MongoDB (NoSQL concepts)

- Azure Data Explorer (KQL)

Data Stack (Mandatory for Screening)

- Databricks Lakehouse - PySpark, SQL, Delta Lake

- AI for BI - Databricks Genie, Genie Rooms, Instructions, Agents

- ETL & Orchestration - Azure Data Factory

- Programming - PySpark

- Cloud Platform - Azure (Preferred)

- DevOps - CI/CD Pipelines, Git

📌 Data Engineer (Pune)
🏢 Bajaj Finance
📍 Pune