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Bajaj Finance

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Data Engineer

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Description

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
  • Strong 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

Skills

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See also

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