Job Summary

To effectively design, develop, and manage data solutions using ETL technologies such as Azure Databricks (ADB), Azure Data Factory (ADF) and SQL along with leading 3 to 5 members of developers

Duties and Responsibilities

- Lead the end-to-end design, development, and delivery of scalable data engineering solutions using Azure Databricks, ADF, PySpark, SQL, and Delta Lake.
- Convert business requirements into robust technical designs, architecture documents, data models, and implementation plans.
- Own technical delivery of data integration, ETL, lakehouse, semantic layer, and AI/BI enablement initiatives.
- Guide and mentor data engineers on coding standards, design best practices, performance optimization, and reusable framework development.
- Review technical designs, code, pipelines, and deployment plans to ensure quality, scalability, maintainability, and compliance.
- Drive architecture decisions for batch and near-real-time data pipelines across Bronze, Silver, and Gold layers.
- Ensure data quality, reconciliation, anomaly detection, and timely resolution of production issues through effective RCA and permanent fixes.
- Optimize data pipelines, Databricks jobs, SQL queries, and storage usage to improve performance and reduce cost.
- Implement CI/CD practices, version control, automated deployments, and environment management across Dev, QA, and Production.
- Collaborate with PMO, business stakeholders, BI teams, InfoSec, DevOps, and external partners for smooth project execution.
- Establish SOPs, engineering standards, reusable components, monitoring frameworks, and documentation practices.
- Track delivery progress,



manage technical dependencies, prioritize work, and ensure timely closure of project milestones.
- Support adoption of modern data platforms, semantic modeling, metrics layer design, and GenAI/BI capabilities.
- Ensure compliance with data governance, security, access control, audit, and enterprise data management standards.
- Act as the technical escalation point for critical issues, complex solutioning, and cross-team dependency resolution.

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
- Strong experience in 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

Positive 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

📌 Associate Delivery Manager - Data Engineer (Pune)
🏢 Bajaj Finance
📍 Pune