Location Name: Pune Corporate Office - Mantri
Job Purpose
To design and deliver AI-powered Business Intelligence solutions by integrating Databricks Genie, 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
KEY ROLES / PRINCIPAL ACCOUNTABILITIES
AI for BI & GenAI Enablement
- Design and implement GenAI-powered BI solutions using Databricks Genie or MS Fabric
- Create and manage Data Rooms for business users with curated datasets
- Define and maintain Instructions Layer (prompt engineering for business context)
- Enable natural language to SQL/insights workflows for self-service analytics
- Drive adoption of Databricks One & other platforms as a unified analytics interface
Semantic Layer & Metrics Engineering
- Design and manage Semantic Models for business abstraction
- Develop reusable and governed Metric Views (KPIs, aggregations, business definitions)
- Ensure consistency across BI tools (Power BI / Genie)
- Align semantic layer with business glossary and data governance policies
Data Engineering & Platform Development
- Build scalable pipelines using Azure Databricks (PySpark, SQL)
- Develop and orchestrate ETL workflows using Azure Data Factory
- Work with Delta Lake architecture (Bronze–Silver–Gold layers)
- Enable real-time and batch data processing pipelines
- Enable data exposure via APIs for BI and downstream systems
CI/CD & DevOps
- Implement CI/CD pipelines for data and AI workflows
- Automate deployments across environments (Dev, QA, Prod)
- Ensure version control and reproducibility
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Key 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
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
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
REQUIRED SKILLS & EXPERIENCE
Must Have
- Azure Databricks – PySpark, SQL, Delta Lake
- 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)
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DATA STACK (MANDATORY FOR SCREENING)
SNoData Platform / ConceptsAssociated Technologies
1Databricks LakehousePySpark, SQL, Delta Lake
2AI for BIDatabricks Genie, Genie Rooms, Instructions, Agents
3Semantic LayerSemantic Models, Metric Views
4ETL & OrchestrationAzure Data Factory
5ProgrammingPython, C#/.NET
6Cloud PlatformAzure (Preferred)
10DevOpsCI/CD Pipelines, Git
