Roles and Responsibilities:

- Build and evolve the EisnerAI lakehouse on ADLS Gen2 and Azure Databricks using a medallion architecture.
- Develop batch and streaming ETL/ELT pipelines for firm source systems, client data, and third-party feeds.
- Create reusable, configuration-driven ingestion frameworks to onboard new data sources rapidly.
- Curate vector-ready data products to ground Retrieval-Augmented Generation (RAG) models via Azure AI Search.
- Implement end-to-end data governance, access controls, and lineage via Unity Catalog and Microsoft Purview.
- Optimize platform performance and manage cloud compute costs through partitioning and Z-ordering.
- Establish CI/CD pipelines, automated data quality checks, and contract testing for pipeline reliability.

Preferred Candidate Profile:

- Experience:



Minimum 4 years of hands-on experience building production data pipelines and platforms.
- Education: Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field.
- Technical Expertise:
- Solid programming skills in Python, SQL, and PySpark at scale.
- Proven delivery on the Azure data stack, including ADLS Gen2, Databricks, and Azure Data Factory.
- Working knowledge of Delta Lake, columnar file formats (Parquet, Avro), and data governance.
- Excellent communication skills to partner with cross-functional AI developers and product owners.

📌 SG Analytics - Data Engineering Manager (India)
🏢 SG Analytics
📍 India