Data Engineer — Wealth Management Platform

Data Engineer — Wealth Management Platform
Austin TX (3-4 days hybrid)
Data & Technology | Wealth Management Division
Department: Data Engineering      
About the Role
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
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
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