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We need Data Engineers who can design, build, and maintain robust data infrastructure on Azure. You’ll build scalable pipelines, ensure data quality, and enable downstream analytics and AI workloads. You’ll work closely with AI engineers, analysts, and business stakeholders to keep data clean, accessible, and reliable. We value engineers who think beyond moving data—people who understand business context and proactively improve systems.
Job Responsibility:
Design and optimize data pipelines using Azure Data Factory, Synapse Analytics, and Databricks
Architect data lakes and warehouses on Azure Data Lake Storage and Synapse
Implement real-time and batch processing with Stream Analytics, Event Hubs, and Databricks Streaming
Ensure data quality, governance, and lineage through validation and monitoring
Build and maintain reliable, scalable ETL/ELT processes
Manage Azure Cosmos DB, Azure SQL Database, and other data stores
Prepare and serve data for ML models and GenAI applications
Support BI/reporting needs via Power BI-ready data models
Own data infrastructure, identify bottlenecks, and drive improvements
Requirements:
Strong hands-on Azure data stack experience: Data Factory, Synapse, Databricks, Data Lake Storage
Proficiency in SQL and Python or Scala for data transformation
Experience building production-grade ETL/ELT pipelines at scale
Familiarity with real-time processing: Stream Analytics, Event Hubs, or Kafka on Azure
Understanding of data modeling, warehousing, and dimensional modeling
Experience with Cosmos DB or other NoSQL databases
Knowledge of data governance and cataloging (Microsoft Purview)
Familiarity with CI/CD for data pipelines (Azure DevOps or GitHub Actions)
Basic Power BI proficiency (reports, dashboards, visualizations)
Must actively use AI-assisted development tools (GitHub Copilot, Cursor, Claude Code, etc.)
Strong problem-solving and communication skills
Nice to have:
Experience with large-scale data systems in enterprise or financial projects
Experience with Delta Lake, Apache Spark, or other big data technologies
Curiosity to explore new tools, frameworks, and problem domains