Data Engineer
Skills
About the role
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 smarter
Preferred 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
Questions about this role
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