Member of Technical Staff | Data Engineering, Finance

Poesis AI; Menlo Park (CA); Hybride; USD 160 000 - 250 000; Plein temps

Avantages offerts par ce poste

Relocation allowance

Catered lunches

Commuter benefits

Résumé du poste

Poesis is building an AI-native investment firm where data quality directly shapes what our models learn and the decisions they make. 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.

Qualifications

• Deep, hands-on experience building production data pipelines and working with financial data.
• Experience with Bloomberg, S&P Capital IQ, FactSet, LSEG/Refinitiv, MSCI Barra, or similar data providers.
• Tackled complex financial data issues involving corporate actions, security mapping and point-in-time accuracy.
• Able to independently troubleshoot complex data issues, work with vendors, and develop practical solutions.
• You've built AI agents or are excited to develop autonomous systems.

Responsabilités

• Build autonomous agents to detect and resolve financial data errors, escalated to humans when needed.
• Address point-in-time accuracy and survivorship bias affecting financial models.
• Investigate data integrity issues, incl. conflicting prices, corporate actions, and security identifiers.
• Build scalable pipelines to integrate new financial datasets and ensure consistency across sources.

Connaissances

Data pipelines

Financial data

Data vendors

Troubleshooting

Autonomous systems

Outils

Snowflake

Databricks

Airflow

AWS

Description du poste

About Poesis

Whoever builds the leading intelligence for finance will create far more than returns. Poesis is the AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. Our founders managed institutional capital at Capital Group ($3T AUM) and led enterprise ML at Goldman Sachs and Amazon. We're building a new type of firm, where live capital is the training ground for an intelligence that compounds with every signal.

About Poesis

Whoever builds the leading intelligence for finance will create far more than returns. Poesis is the AI-native investment firm running autonomous agents that predict markets, construct portfolios, and manage risk. Our founders managed institutional capital at Capital Group ($3T AUM) and led enterprise ML at Goldman Sachs and Amazon. We're building a new type of firm, where live capital is the training ground for an intelligence that compounds with every signal.

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 escalation 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.

Working at Poesis

As an early team member, you’ll help shape not just the product, but how the company operates. Your decisions will have lasting impact across the business. You’ll build from first principles, with no legacy systems, or entrenched processes slowing you down. Our team is made up of people from elite companies and universities who are low ego, collaborative, and excited to build together.

Compensation Range: $160K - $250K