Data Engineer

Role Overview

• Build and optimize large-scale data pipelines
• Work with wealth management data including positions, transactions, and accounts
• Utilize Databricks for data engineering tasks
• Collaborate with teams on AI tool implementation in daily work

Requirements

• 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

Tech Stack

• Azure
• Cloud
• ETL
• PySpark
• Python
• Unity