Data Engineer

Required Qualifications 5-8 years of experience in data engineering, with direct exposure to wealth management data domains Databricks Certified (Associate or Professional) or demonstrated deep, hands-on Databricks expertise in a production environment Proficiency in Python and PySpark for building and optimizing large-scale data pipelines Hands-on experience with Microsoft Azure cloud services (Azure Data Factory, Azure Data Lake Storage, Azure Synapse, or equivalent) Direct experience working with wealth management data including positions, transactions, accounts, clients, advisors, and security master data Experience reconciling financial datasets across custodians, platforms, or internal systems Strong understanding of data modeling, ETL/ELT patterns, and data warehouse or lakehouse architecture Demonstrated use of AI tools in day-to-day engineering work — this is not optional; we expect engineers to be actively leveraging AI to move faster and work smarterPreferred Qualifications Experience with Delta Lake, Unity Catalog, or Databricks Asset Bundles Familiarity with custodial data feeds and formats (Schwab, Fidelity, Pershing, or similar) Exposure to advisor technology platforms such as Addepar, Black Diamond, Envestnet, Orion, or Tamarac Experience with dbt (data build tool) for transformation layer development Knowledge of financial instruments including equities, fixed income, alternatives, and managed accounts Familiarity with data governance, data lineage, and metadata management practices Experience in a fintech, WealthTech, RIA, or asset management environment