Data & BI Lead

Overview :

The ideal candidate will lead and deliver end-to-end data platforms and BI solutions, with equal depth in data engineering, data modelling, and BI engineering (Power BI)

Key Responsibilities:

• Lead design and implementation of scalable data platforms (lakehouse / warehouse) on Microsoft Fabric, Azure Databricks, or Snowflake
  
• Build and optimize data ingestion, transformation, and ELT pipelines for large-scale data processing
  
• Design and implement enterprise data models across:
  • Data warehouse / lakehouse layers
    
  • Analytical / semantic layers for reporting
    
• Develop and maintain Power BI datasets, semantic models, and dashboards aligned with enterprise standards
  
• Drive query and performance optimization across:
  • SQL workloads and data pipelines
    
  • BI semantic layer and reporting performance
    
• Establish data warehousing standards, modelling frameworks, and reusable data assets
  
• Ensure data quality, governance, lineage, and security across the platform
  
• Collaborate with stakeholders to translate business requirements into scalable data and analytics solutions
  
• Provide technical leadership across both data engineering and BI workstreams
  
• Drive DevOps and CI/CD practices for data pipelines and BI deployments
  

Required Skills & Experience:

• 10+ years in Data Engineering, Data Warehousing, BI, or Analytics
  
• Strong hands-on expertise in data engineering on at least one platform (mandatory):
  • Microsoft Fabric (Lakehouse, Dataflows, Pipelines)
    
  • Azure Databricks (PySpark, Delta Lake, distributed processing)
    
  • Snowflake (ELT design, performance optimization, data modelling)
    
• Very strong SQL expertise (mandatory):
  • Complex transformations, query optimization, and performance tuning
    
  • Experience handling large-scale, high-volume datasets
    
• Deep expertise in Data Modelling (mandatory):
  • Dimensional modelling (star/snowflake schema)
    
  • Normalization vs denormalization strategies
    
  • Semantic layer design for analytics
    
• Strong Data Warehousing experience (mandatory):
  • Designing and implementing enterprise-grade data warehouses
    
  • Strong understanding of Kimball/Inmon methodologies
    
  • Experience with lakehouse architectures
    
• Strong Power BI experience (mandatory):
  • End to end development expertise
    
  • Building semantic models, reports, and dashboards
    
  • Advanced DAX and Power Query (M)
    
  • Performance tuning (aggregations, incremental refresh, composite models)
    
• Experience with:
  • Data orchestration tools (e.g., Azure Data Factory / Synapse or equivalent)
    
  • Python / PySpark for data processing
    
  • CI/CD and DevOps practices for data and analytics solutions
    
  • Data governance, lineage, and security (including RLS/OLS)
    

Good to Have:

• Exposure to AI / GenAI / Agentic AI concepts in data and analytics workflows
  
• Experience with Tableau
  

Ideal Candidate Profile:

• Strong foundation in data engineering, data modelling, and BI engineering
  
• Capable of designing and building both backend data platforms and frontend analytical solutions
  
• Comfortable working across architecture, development, and delivery
  
• Effective in engaging with both business stakeholders and technical teams
  
• Focused on scalability, performance, and long-term maintainability