WHO WE ARE

Addepar is a global technology and data company that helps investment professionals provide the most informed, precise guidance for their clients. Hundreds of thousands of users have entrusted Addepar to empower smarter investment decisions and better advice over the last decade. With client presence in more than 50 countries, Addepar’s platform aggregates portfolio, market and client data for over $7 trillion in assets. Addepar’s open platform integrates with more than 100 software, data and services partners to deliver a complete solution for a wide range of firms and use cases. Addepar embraces a global flexible workforce model with offices in Silicon Valley, New York City, Salt Lake City, Chicago, London, Edinburgh, Pune, and Dubai.

THE ROLE

We’re looking for a Senior Portfolio Data Engineer to join our Data Engineering team in Edinburgh.
A core part of Addepar’s business relies on us being able to quickly and correctly ingest data from a variety of sources, including 3rd party data providers, custodial banks, data APIs, and even direct user input. The data engineers on the Portfolio Data Engineering team help build and maintain the transformation and cleaning steps of our ETL (Extract, Transform, Load) pipeline before it can be stored and accessed by our customers in a standardised fashion. As a data engineer on this team, you’ll be building components within the ETL pipeline that automate these cleaning and transformation steps. As you gain more experience, you’ll contribute to increasingly challenging engineering projects within our broader data infrastructure.
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 data cleaning effort required from our clients or from our internal data operations team.

WHAT YOU’LL DO

• Write code and design pipeline architecture.
• Build pipelines that support the ingestion, analysis, and enrichment of financial data.
• Improve the existing pipeline to increase the throughput and accuracy of data.
• Work with data analysts to map general financial concepts to our internal data models in a repeatable and precise fashion.
• Understand data models and schemas, and work with other engineering staff to recommend extensions and changes.
• Use investigative tools and database queries to automatically flag irreconcilable data within our production datasets.