Senior Data Engineer

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.

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 (BronzeSilverGold 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

• 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

• 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

• 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

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)

• Databricks Lakehouse -- PySpark, SQL, Delta Lake
• AI for BI -- Databricks Genie, Genie Rooms, Instructions, Agents
• Semantic Layer -- Semantic Models, Metric Views
• ETL Orchestration -- Azure Data Factory
• Programming -- Python, C#/.NET
• Cloud Platform -- Azure (Preferred)
• DevOps -- CI/CD Pipelines, Git

• Strong analytical and problem-solving mindset
• Ability to bridge business + data + AI
• Effective stakeholder communication
• Ownership-driven and proactive approach
• Ability to work in fast-paced, evolving AI landscape