Data Engineer

Data Engineer Wealth Management Platform

Employment Type: Full-Time

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.

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