Azure Data Warehouse Developer

Job Description:

We are seeking an 5+ Years experienced Azure Data Warehouse Developer to join our growing data team. The ideal candidate will have a deep understanding of cloud-based data architecture, specifically within Microsoft Azure, and will play a key role in designing, implementing, and maintaining data warehouse solutions on Azure. You will collaborate with cross-functional teams to ensure scalable and high-performance data solutions.

Roles & Responsibilities :
Designing and implementing dimensional models — star and snowflake schemas, slowly changing dimensions (SCD Types 1, 2,
3), and fact/dimension table strategies
Writing complex, optimized T-SQL including window functions, CTEs, dynamic SQL, and query tuning
Table design considerations within Fabric Warehouse: distribution strategies, indexing, partitioning, and statistics management
Data quality enforcement through constraints, validation logic, and reconciliation frameworks
Understanding of Fabric Warehouse's current limitations vs. Azure Synapse Dedicated SQL Pools, and how to work within them
Hands-on Synapse Analytics experience — dedicated/serverless SQL pools, distribution strategies
Deep ADLS Gen2 knowledge — hierarchical namespace, Parquet/Delta file formats, partitioning strategies
Storage security — RBAC, ACLs, managed identities, private endpoints
Managing large-scale file formats:
Parquet, Delta, CSV, JSON, and Avro
Experience connecting to source systems: databases, REST APIs, flat files, SaaS platforms
Query and storage performance tuning — file size optimization, indexing, partitioning
Hands-on experience with Fabric's T-SQL Warehouse and understanding how it differs from Synapse
Knowledge of cross-database and cross-item queries spanning Lakehouse and Warehouse
Understanding of Fabric's V-Order optimization and Delta Lake file management
Identifying and resolving data skew, spill, and shuffle issues in Spark workloads
Monitoring capacity usage, query execution plans, and resource contention
Experience building semantic models directly on top of Fabric Warehouse or Lakehouse
Understanding of when to choose Fabric DW over Lakehouse SQL endpoint for a given workload
One Lake architecture — shortcuts to ADLS Gen2, Lakehouse vs. Warehouse trade-offs
Medallion architecture implementation (Bronze/Silver/Gold)
Lakehouse mirroring and real-time data ingestion patterns
Fabric Data Pipelines, Notebooks (PySpark/SQL), and Dataflows Gen2
Delta Lake operations — OPTIMIZE, VACUUM, Z-ordering, time travel
Incremental load strategies: watermark patterns, change data capture (CDC), and merge/upsert logic
Implementing row-level security (RLS) and column-level security (CLS) in Fabric Warehouse
Managing workspace roles, item permissions, and One Lake access controls
Understanding of data lineage tracking and cataloging within Fabric
Compliance awareness: GDPR, HIPAA, or industry-specific data handling requirements

Required Skills:

Direct Lake mode in Power BI and semantic model design
End-to-end security model spanning ADLS and Fabric
Row-level and column-level security in DW and semantic layers
Sensitivity labels and Microsoft Information Protection
Designing end-to-end solutions where ADLS underpins Fabric via One Lake shortcuts
ELT/ETL pattern selection and pipeline orchestration
Microsoft Purview for governance and lineage across ADLS and Fabric
Multi-workspace and multi-domain architecture patterns for enterprise environments
Disaster recovery, backup strategies, and SLA considerations within Fabric
Fabric REST APIs and CI/CD using Azure Dev Ops or Git Hub
Comfortable with Fabric's rapid evolution and incomplete documentation

Experience with integrating with Human in the middle workflows