5-7 years of experience in data engineering within a quantitative investment firm or similar environment.
Hands-on experience with financial data and production data pipelines.
Familiarity with major financial data providers like Bloomberg and FactSet.
Proven ability to troubleshoot complex data integrity issues independently.
Experience in building or a strong interest in developing AI agents for real-world problem-solving.
Responsibilities
Build autonomous agents to detect and resolve financial data errors.
Address challenges like point-in-time accuracy and survivorship bias.
Investigate and resolve complex data integrity issues, including conflicting prices.
Develop scalable pipelines for integrating new financial datasets.
Ensure consistency across various financial data sources.
Benefits
Excellent medical, dental, and vision coverage.
Catered lunches provided.
Commuter benefits available.
Relocation allowance offered for new hires.
Full Job Description
About the Role
At Poesis, we're building an AI-native investment firm where data quality directly shapes what our models learn and the decisions they make. Financial data is complex, and making it accurate, consistent and reliable at scale is one of our most important engineering challenges. As a Member of Technical Staff focused on data engineering, you'll build reliable autonomous AI agents that detect and resolve complex data issues and strengthen the foundation of our investment process. This is an opportunity to pioneer a new approach to financial data engineering, with AI at the core of how data systems are built and operated.
Challenges You'll Work On
Build reliable autonomous agents that detect, investigate and resolve financial data errors, knowing when to escalate to humans.
Tackle point-in-time accuracy, survivorship bias and other challenges that can undermine financial models.
Investigate and resolve complex data integrity issues, from conflicting prices and corporate actions to inconsistent security identifiers.
Build scalable pipelines to integrate new financial datasets and ensure consistency across sources.
About You
You have deep, hands-on experience building production data pipelines and working with financial data at a quantitative investment firm, data provider or similar environment.
You have experience with Bloomberg, S&P Capital IQ, FactSet, LSEG/Refinitiv, MSCI Barra, or similar data providers.
You have tackled complex financial data issues involving corporate actions, security mapping and point-in-time accuracy, and understand their impact on backtesting and risk models.
You can independently troubleshoot complex data issues, work with vendors when needed and develop practical solutions.
You've built AI agents or are excited to develop autonomous systems that solve complex, real-world problems.
Nice to Have
Experience with Snowflake, Databricks, Airflow, AWS, or related technologies.
Location
Hybrid, 3 days per week on-site at our office in Menlo Park, CA. Relocation allowance available.
Benefits
We offer excellent medical, dental, and vision coverage, alongside a strong benefits package that includes catered lunches, commuter benefits, and more.
Current legal authorization to work in the US required; continuing work visa sponsorship available for full-time employees.
