Job Purpose
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
KEY 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 contemporary 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
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
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
REQUIRED SKILLS & 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
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

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