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 recent 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: Robust 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.