Data Engineer - Wealth Management Platform

We are seeking a skilled Data Engineer with a strong wealth management background to join our data and technology team. This role sits at the intersection of financial data and modern cloud engineering - you will design, build, and maintain the data pipelines and infrastructure that power our advisor and client reporting, reconciliation processes, and platform integrations. The ideal candidate brings hands-on experience with Databricks and the Microsoft cloud ecosystem, a deep understanding of wealth management data domains, and the ability to leverage AI tooling to accelerate their daily work. Key Responsibilities Data Pipeline Development & Engineering

• Design, build, and maintain scalable data pipelines using Databricks and Azure cloud services
• Develop and optimize PySpark and Python-based ETL/ELT workflows for ingesting, transforming, and serving wealth management data
• Build and manage data models that support advisor, account, client, position, transaction, and security datasets
• Ensure data pipelines meet performance, reliability, and latency requirements for downstream consumers

Financial Data & Reconciliation

• Reconcile financial datasets across custodians, internal systems, and third-party data providers - identifying and resolving breaks at the position, transaction, and account level
• Partner with operations and service teams to investigate and resolve data discrepancies impacting advisors and clients
• Implement data quality checks, validation rules, and alerting to proactively catch data integrity issues
• Support the build-out of reconciliation frameworks that scale across growing data volumes and entity counts

Cloud Infrastructure & Platform

• Build and manage data infrastructure on Microsoft Azure, including Azure Data Factory, Azure Data Lake, and related services
• Contribute to the architecture and governance of the data lakehouse environment within Databricks (Delta Lake, Unity Catalog)
• Collaborate with platform and DevOps teams on CI/CD pipelines, environment management, and data infrastructure as code

AI-Augmented Engineering

• Actively leverage AI coding assistants and automation tools (e.g., GitHub Copilot, Claude, ChatGPT) to accelerate development, code review, and documentation
• Identify opportunities to apply AI/ML techniques to financial data problems such as anomaly detection, break prediction, or data classification
• Stay current on emerging AI tooling and bring practical recommendations to the team

• 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 Key Competencies

• Financial Data Fluency - You speak the language of wealth management data and understand what positions, transactions, and reconciliation breaks mean to the business
• Engineering Rigor - You write clean, testable, well-documented code and care about the reliability of what you build
• AI-Forward Mindset - You actively incorporate AI tools into your workflow and treat them as force multipliers, not novelties
• Cross-Functional Collaboration - You can work effectively with operations, service, and product teams to understand data needs and translate them into engineering solutions
• Problem Ownership - You don’‘t just find issues in data; you see them through to resolution and build guardrails to prevent recurrence