Data Engineer

The Role

Portfolio Data Integration is part of the broader Addepar Platform team. The overall Addepar Platform provides a single source of truth “data fabric” used by the Addepar product set, including a centralized and self-describing repository (a.k.a Data Lake), a set of API-driven data services, an integration pipeline, analytics infrastructure, warehousing solutions, and operating tools. The team has responsibility for all data acquisition, conversion, cleansing, disambiguation, modeling, tooling and infrastructure related to the integration of client portfolio data.

Addepar’s core business relies on the ability to quickly and accurately ingest data from a variety of sources, including 3rd party data providers, custodial banks, data APIs, and even direct user input. Portfolio Data integrations and feeds are a highly critical cross-section of this set, allowing our users to get automatically updated and reconciled information on their latest holdings onto the platform.

As a data engineer for this team, you will execute the development of new data integrations and maintenance of existing processes in order to expand and improve our data platform. You’ll be adding automation and functionality to our distributed data pipelines by writing PySpark code and integrating it within our Databricks Data Lake. The team operates with an AI-augmented engineering workflow — LLM tools are integrated into code review, testing, and documentation as standard practice, not an experiment. As you gain more experience, you’ll contribute to increasingly challenging engineering projects within our platform with the ultimate goal of dramatically increasing the throughput of data ingestion for Addepar. This is a crucial, highly visible role within the company. Your team is a big component of growing and serving Addepar’s client base with minimal manual effort required from our clients or from our internal data operations team.

Applicants must have, and maintain, the right to work in the United Kingdom from the first day of employment. Please note that visa sponsorship is not available for this role.

What You’ll Do

• Complete individual project priorities, deadlines, and solutions.
• Build pipelines that support the ingestion, analysis, and enrichment of financial data in partnership with business data analysts
• Improve the existing pipeline to increase the throughput and accuracy of data
• Develop and maintain efficient process controls and accurate metrics to ensure quality standards and organizational expectations are met
• Partner with members of Product and Engineering to design, test, and implement new processes and tooling features that improve data quality as well as increase operational efficiency
• Identify areas of automation opportunities and implement improvements
• Understand data models and schemas, and work with other engineering teams to recommend extensions and changes
• Use AI-assisted development workflows — The team uses LLM tools (Claude Code) as a standard part of the engineering workflow: pre-review, test generation, code analysis, and documentation. You'll be expected to adopt and contribute to these workflows
• Maintain and improve automated quality gates — Work with AI-powered code review tooling to enforce coding standards, catch regressions before human review, and reduce manual toil in the PR process

Who You Are

• Prior professional data engineering/analysis experience
• Experience with object-oriented programming (we use PySpark/Python)
• Knowledge of SQL or relational database concepts
• Experience in data modeling, visualization, and ETL pipelines
• Comfort working with AI/LLM tools as part of your development workflow (e.g., code assistants, automated review, AI-generated tests). You don't need to be an expert — but you should be curious and willing to adopt new tooling quickly
• Knowledge of financial concepts (e.g., stocks, bonds, etc.) is helpful but not necessary
• Passion for the world of FinTech and solving previously intractable problems at the heart of investment management