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 OneLake 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 DevOps or GitHub Comfortable with Fabric's rapid evolution and incomplete documentation Experience with integrating with Human in the middle workflows