Addepar is a global data and AI platform empowering investment professionals to turn complex financial information into actionable intelligence. Addepar unifies portfolio, market and client data in a total portfolio view and delivers AI-powered insights within investment and client workflows. More than 1,400 firms in nearly 60 countries use Addepar to manage and advise on nearly $9 trillion in assets. Its open platform integrates with nearly 650 software, data and consulting partners to power end-to-end investment operations across firms of all sizes and complexity. Addepar supports clients worldwide with offices in New York City, Salt Lake City, London, Edinburgh, Pune, Dubai, Geneva and São Paulo.

Portfolio Data Engineers work on building and maintaining data feed pipelines that import financial data into Addepar's platform. They partner closely with Portfolio Data Analysts to implement technical solutions for processing portfolio data from various financial institutions and data providers. Please note: applicants must have, and maintain the right to work in Poland to be considered. A background check is required to be completed prior to the first day of work.

•Code Implementation: Responsible for implementing logic, writing, and merging code for data integrations •Testing: Creating unit tests, regression testing in staging environments, and iterating until expected results are achieved •Technical Architecture: Working on high-level financial data pipeline architecture and data transformation design •Collaboration: Partnering with Product Analysts on data research and analysis tasks •Code Ownership: Maintaining code quality and ensuring technical requirements are met

• Experience with data pipeline architecture • Understanding of data transformations and ETL processes • Ability to work with complex data structures • Experience with Databricks • Strong coding abilities in Python •Ability to quickly learn unfamiliar technologies and frameworks (Addepar leverages a custom framework on top of PySpark to orchestrate data transformations in a standardized way, which needs to be quickly learnt and conformed to) • Some financial knowledge is beneficial but not required