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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.
- Robust 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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Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data Engineer (India)
🏢 Suzega
📍 India