Data Engineering Manager

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: Strong programming skills in Python, SQL, and PySpark at scale.
• Technical Expertise: Proven delivery on the Azure data stack, including ADLS Gen2, Databricks, and Azure Data Factory.
• Technical Expertise: Working knowledge of Delta Lake, columnar file formats (Parquet, Avro), and data governance.
• Assumed Requirements: Excellent communication skills to partner with cross-functional AI developers and product owners.