Data Engineer

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Own end-to-end data modernization on Azure Synapse or Fabric, building scalable pipelines from client requirements to delivery, and providing governed, high-quality data ready for AI/ML and natural-language search.

What you d do

• Design, build, and optimize scalable ETL/ELT data pipelines using advanced T-SQL and Python within Azure Synapse Dedicated SQL Pools and Azure Data Hub.
• Develop and manage medallion architecture schemas (Bronze, Silver, Gold) optimized for high-performance SQL analytics and AI-powered workloads.
• Integrate AI capabilities into data operations, including automated incident triage, cost-reduction recommendations, and proactive pipeline monitoring.
• Implement data quality checks and monitoring frameworks.
• Manage and administer data warehouses, data lakes, and databases (SQL/NoSQL).
• Implement and manage workflow orchestration tools (e.g., Airflow, Prefect, Dagster) for scheduling and monitoring data pipelines.
• Collaborate with AI/ML Engineers to ground LLM applications in governed data through vector embeddings and semantic search metadata.
• Ensure data security and compliance standards are met.
• Optimize storage and processing costs across the Azure Stack, utilizing FinOps automation and workload tuning.
• Write efficient and maintainable Python code for data processing tasks.
• Work independently to troubleshoot and resolve data-related issues.

What we re looking for

• Expertise in T-SQL and deep experience with Azure Synapse Dedicated SQL Pools, Azure Data Hub, and relational/NoSQL databases.
• Strong proficiency in Python for data manipulation, AI orchestration, and pipeline development (e.g., PySpark, Pandas).
• Hands-on experience with the Azure Stack, including Synapse Pipelines, ADLS Gen2, and Azure Data Hub for enterprise-scale DWH operations.
• Familiarity with AI integration patterns, such as prompt engineering, vector databases, and semantic layer management for natural-language query tools.
• Experience building and managing data pipelines and ETL/ELT processes.
• Familiarity with data warehousing concepts and data modeling.
• Understanding of data quality principles.
• Ability to work independently and take ownership of data infrastructure components.

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