Addepar - Data Engineer
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Requirements
• 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 • Our Values • Act Like an Owner - Think and operate with intention, purpose and care. Own outcomes. • Act Like an Owner - • Build Together - Collaborate to unlock the best solutions. Deliver lasting value. • Build Together - • Champion Our Clients - Exceed client expectations. Our clients’ success is our success. • Champion Our Clients - • Drive Innovation - Be bold and unconstrained in problem solving. Transform the industry. • Drive Innovation - • Embrace Learning - Engage our community to broaden our perspective. Bring a growth mindset. • Embrace Learning -
Responsibilities
• 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
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