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Senior Data Engineer

Datavail Infotech Pvt. Ltd.
Remote, USA
Full-time
Senior · 8+ years experience
Remote
Discovered 4 days ago

Full Job Posting

Requirements

  • 8+ years in data engineering/data warehousing; 3+ years delivering Azure solutions and production

Full Job Posting

  • 8+ years in data engineering/data warehousing; 3+ years delivering Azure solutions and production
  • Microsoft Fabric experience
  • Advanced T-SQL, Spark SQL, Python/PySpark; working knowledge of KQL and DAX
  • Deep experience with Delta Lake, Spark tuning, medallion/lakehouse patterns, CDC and incremental loading
  • Strong dimensional modeling and working knowledge of Data Vault 2.0
  • Practical CI/CD, Git, automated testing, infrastructure as code, DataOps, governance and security experience
  • Professional-services/client-facing delivery experience
  • Authorization to work in the US and ability to travel up to 15%
  • Preferred: DP-700, DP-600, DP-203; streaming/event-driven, RAG/GenAI, Fabric IQ/data agents, and industry experience
  • Design, build and maintain production-grade batch, incremental, real-time and near-real-time data pipelines across Microsoft Fabric and Azure
  • Implement CDC, watermarking, merge/upsert, late-arriving-data and idempotent reprocessing patterns
  • Develop PySpark, Spark SQL and T-SQL transformations across bronze, silver and gold Delta Lake layers
  • Build Fabric solutions using OneLake, Lakehouse, Warehouse, Data Factory, Dataflows Gen2, Spark notebooks, Real-Time Intelligence and Power BI
  • Implement dimensional and Data Vault models, data-quality testing, reconciliation, observability, logging, alerting and SLA monitoring
  • Establish Git-based CI/CD and automated deployment using Azure DevOps or GitHub, Fabric deployment pipelines, REST APIs, Bicep and Terraform
  • Apply Purview catalog/lineage, least-privilege access, sensitivity controls, encryption and secure connectivity
  • Support migrations from SQL Server, SSIS, SSAS, legacy warehouses and Synapse to Fabric/Azure, including conversion, reconciliation and cutover
  • Prepare governed data for AI/ML and RAG workloads, including ingestion, chunking, embeddings, indexing and telemetry
  • Lead engineering workstreams, estimates, risk management, client communication, mentoring, reusable accelerators and operational handover

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