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

KEY 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 recent 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

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 integrationMajor Challenges

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 teamsRequired Qualifications and Experience

REQUIRED SKILLS & EXPERIENCE

Must Have
Azure Databricks – PySpark, SQL, Delta Lake
Solid experience in Semantic Modeling & Metrics Layer design
Hands-on with Databricks workflows
Pyspark (Pandas, PySpark, FastAPI)
Azure Data Factory (ADF) for ETL pipelines
Robust SQL and data modeling skills

Good to Have
Cosmos DB / MongoDB (NoSQL concepts)
Azure Data Explorer (KQL)

DATA STACK (MANDATORY FOR SCREENING)

SNo Data Platform / Concepts Associated Technologies

1 Databricks Lakehouse PySpark, SQL, Delta Lake

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

4 ETL & Orchestration Azure Data Factory

5 Programming Pyspark

6 Cloud Platform Azure (Preferred)

10 DevOps CI/CD Pipelines, Git

📌 Data Engineer Pune (India)
🏢 Bajaj Finance
📍 India