India · Senior
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| Job Purpose | To design and deliver AI-powered Business Intelligence solutions by integrating Databricks Genie, PowerBI Co-Pilot & Semantic Models, and Metric Views on top of a robust end-to-end data engineering stack. The role focuses on enabling self-service analytics using natural language (GenAI), ensuring business-friendly data consumption, and building governed, scalable, and high-performance data ecosystems across batch and real-time pipelines. |
| Duties and Responsibilities | • Translate business problems into AI-driven BI solutions • Own end-to-end delivery: ingestion ? transformation ? semantic layer ? AI consumption • Design scalable data architecture aligned with lakehouse principles • Ensure data quality, governance, and metric consistency • Collaborate with business teams to onboard them onto Genie-based analytics • Optimize performance of queries, pipelines, and AI responses • Establish best practices for AI in BI (prompting, semantic tuning, governance) • Drive adoption of self-service BI with minimal dependency on tech teams |
| Key Decisions / Dimensions | • Define semantic layer design and metric definitions • Decide GenAI prompting strategies and instruction frameworks • Prioritize data vs AI optimization trade-offs • Handle production issues with RCA and long-term fixes • Drive architectural decisions for lakehouse + AI integration |
| Major Challenges | • Ensuring accuracy and trust in AI-generated insights • Driving adoption of GenAI-based BI over traditional dashboards • Maintaining semantic consistency across multiple tools • Balancing performance, cost, and scalability • Managing dependencies across data engineering, AI, and business teams |
| Required Qualifications and Experience | • Azure Databricks – PySpark, SQL, Delta Lake • PowerBI – CoPilot, Semantic Model • Strong experience in Semantic Modeling & Metrics Layer design • Hands-on with Databricks Genie / GenAI-based BI workflows • Databricks One (Unified BI Experience) • Prompt Engineering / Instruction tuning for AI systems • Python (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, PowerBI CoPilot 3 Semantic Layer Semantic Models, Metric Views 4 ETL & Orchestration Azure Data Factory 5 Programming Python, C#/.NET 6 Cloud Platform Azure (Preferred) 7 DevOps CI/CD Pipelines, Git |
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