Senior Data Engineer

Take ownership of end-to-end data modernization on Azure Synapse or Fabric, translating client requirements into scalable pipelines and delivering governed, high-quality data products. The work supports downstream AI/ML applications and natural-language search experiences.

• Design, build, and optimize scalable ETL/ELT 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) tuned for high-performance SQL analytics and AI-ready 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 that keep datasets trustworthy.
• Administer data warehouses, data lakes, and relational plus No
  
  SQL databases.
• Operate workflow orchestration tools such as Airflow, Prefect, or Dagster for scheduling and monitoring pipelines.
• Collaborate with AI/ML engineers to ground large language model applications through vector embeddings and semantic search metadata.
• Maintain data security and compliance standards, and tune storage plus processing costs using Fin Ops automation.
• Troubleshoot and resolve data issues independently.

• Expertise in T-SQL with deep experience using Azure Synapse Dedicated SQL Pools, Azure Data Hub, and relational plus No
  
  SQL databases.
• Strong Python proficiency for data manipulation, AI orchestration, and pipeline development, including PySpark and Pandas.
• Hands-on experience across the Azure Stack, including Synapse Pipelines, ADLS Gen2, and Azure Data Hub for enterprise data warehouse 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 at scale.
• Solid grasp of data warehousing concepts, data modeling, and data quality principles.
• Ability to work independently and take ownership of data infrastructure components.