Portfolio Analytics Architect

Addepar•Salt Lake City, UT

13h•$103,000 - $129,000

About The Position

A Portfolio Analytics Architect is responsible for leading the data validation process end-to-end with clients for data conversion projects. The ideal candidate will have a strong understanding of financial portfolio data, a motivation to work on data problems, outstanding communication skills and the ability to deliver results in adherence with project deadlines while meeting high quality standards. They are passionate about understanding our clients needs, take a hands-on approach to solving problems, work collaboratively with internal teams and take ownership of our clients success.

Requirements

B.S in Finance, Mathematics, Statistics, Business or Economics
2+ years experience working in Technology, Finance, or Consulting
Hands-on experience working with complex investment datasets, including multi-asset class portfolios, performance data, and market data from various sources
Proficiency in Python, Spark, and/or SQL with familiarity of ETL fundamentals
Working knowledge of REST APIs
Experience with Databricks and/or a cloud-based distributed data platform
Solution-oriented approach and a passion for problem solving
Proven ability to research, design and implement solutions for complex problems
Positive attitude, strong work ethic, proactive and a high contributing team player
Independent, adaptable and can thrive in a fast-paced environment
Excellent communication and presentation skills, with proficiency in handling client objections

Responsibilities

Lead the data validation process end-to-end with the client and internal projects teams
Develop scalable data analytics processes for validation in Databricks
Write custom Python logic to detect large-scale data anomalies and automate remediation
Facilitate recurring client sessions to track progress and steer the data validation project through to completion.
Support clients with researching and identifying portfolio data performance discrepancies and make recommendations on how to best fix manual data issues
Review, compare and document system calculation methodologies differences
Coordinate across project teams communicating regular status updates for assigned data projects and while effectively setting expectations
Mitigate project risks by effectively escalating key impediments to stakeholders and executive leadership.
Prioritize and context-switch effectively to complete simultaneous projects; seeing each through to the finish line
Identify and implement AI-driven automation and LLM integrations to streamline implementation