Member of Technical Staff | Data Engineering, Finance

Poesis•Menlo Park, CA

13d•$160,000 - $250,000•Hybrid

About The Position

Poesis is an AI-native investment firm that runs autonomous agents to predict markets, construct portfolios, and manage risk. The Member of Technical Staff focused on data engineering will build reliable autonomous AI agents to detect and resolve complex financial data issues and strengthen the foundation of the investment process. The role centers on making financial data accurate, consistent, and reliable at scale, pioneering an approach to financial data engineering with AI at the core of how data systems are built and operated. This is an early-team role with the opportunity to shape both the product and company operations. The position is hybrid, with three days per week on-site at Poesis’s Menlo Park, California office. Relocation allowance is available. Current legal authorization to work in the United States is required; continuing work visa sponsorship is available for full-time employees.

Requirements

Deep, hands-on experience building production data pipelines and working with financial data at a quantitative investment firm, data provider, or similar environment.
Experience with Bloomberg, S&P Capital IQ, FactSet, LSEG/Refinitiv, MSCI Barra, or similar data providers.
Experience addressing complex financial data issues involving corporate actions, security mapping, and point-in-time accuracy, and understanding of their impact on backtesting and risk models.
Ability to independently troubleshoot complex data issues, work with vendors when needed, and develop practical solutions.
Experience building AI agents or interest in developing autonomous systems that solve complex, real-world problems.
Current legal authorization to work in the United States.

Nice To Haves

Experience with Snowflake, Databricks, Airflow, AWS, or related technologies.

Responsibilities

Build reliable autonomous agents that detect, investigate, and resolve financial data errors, escalating to humans when appropriate.
Address point-in-time accuracy, survivorship bias, and other issues that can undermine financial models.
Investigate and resolve complex data integrity issues, including conflicting prices, corporate actions, and inconsistent security identifiers.
Build scalable pipelines to integrate new financial datasets and ensure consistency across sources.
Troubleshoot complex data issues independently, work with vendors when needed, and develop practical solutions.