Data Engineer

Qode • Arlington, TX

6d

About The Position

We are seeking a skilled Data Engineer with a strong background in data engineering and direct exposure to wealth management data domains. The ideal candidate will have significant experience with Databricks, Python, PySpark, and Microsoft Azure cloud services. This role involves building and optimizing large-scale data pipelines, working with complex financial datasets, and leveraging AI tools to enhance engineering efficiency. A deep understanding of data modeling, ETL/ELT patterns, and data architecture is crucial. The position requires hands-on experience with wealth management data, including positions, transactions, accounts, clients, advisors, and security master data, as well as experience in reconciling financial datasets across various systems.

Requirements

• 5–8 years of experience in data engineering.
• 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.

Nice To Haves

• 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.

Responsibilities

• Build and optimize large-scale data pipelines using Python and PySpark.
• Utilize Microsoft Azure cloud services such as Azure Data Factory, Azure Data Lake Storage, and Azure Synapse.
• Work with wealth management data including positions, transactions, accounts, clients, advisors, and security master data.
• Reconcile financial datasets across custodians, platforms, or internal systems.
• Apply data modeling, ETL/ELT patterns, and data warehouse or lakehouse architecture principles.
• Actively leverage AI tools in day-to-day engineering work to improve efficiency and speed.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed