India · Mid Level
Applicants who checked fit first are 3.1× more likely to hear back
Your score for this role already exists
ASAI compared this JD against 41 signals - skills, seniority, domain, stack overlap etc. Add a resume and it unlocks in about 30 seconds.
Always free · no credit card · 1 tap with Google
LOW
Our tracked job listings for Bajaj Finance show limited recent activity.
First 72 hours
Window passed - posted 58d agoEarly applicants get seen before the pile builds.
Not a repost
The first time we've seen this listing - it hasn't been closed and reopened.
You almost certainly match several of these already. Unlock your skill map to see the matches, the gaps, and what to fix first.
| Job Purpose | To effectively design, develop, and manage data solutions using ETL technologies such as Azure Databricks (ADB) , Azure Data Factory (ADF) and SQL |
| Duties 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 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 | 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 |
Your match score, skill map and tracker stay free.
Skip jobs with hidden dealbreakers. See what they actually need, what's easy to miss, and how they describe the team.
Ground Reality on every job, plus Insider Path, practice interviews and resume tailoring.
We help you find people there who worked or studied where you did, and draft your first message and follow-up.
Free · no signup
Daily job drops, skill trends and free resources - posted straight to the group. Leave any time.
No spam. Just jobs and resources.
Why people use ASAI
Scored, not searched. Every role ranked against your actual profile.
Alerts as often as hourly. Reach new roles while the pile is still small.
Skill gaps, spelled out. See exactly which requirements you don't meet yet.
Verified jobs, only. Say no to ghost jobs. Your time deserves respect.
Keep browsing