•      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 smarter

•      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