Pay
$127K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Menlo Park · Menlo Park, United States
Actively hiring, first seen by us 5 days agoLocation type: HybridFrom the employer’s posting
Employment type: Full-timeFrom the employer’s posting
Department: EngineeringFrom the employer’s posting
At Poesis, we're building an AI-native investment firm where data quality directly shapes what our models learn and the decisions they make.
Build reliable autonomous agents that detect, investigate and resolve financial data errors, knowing when to escalate to humans.
Build autonomous agents that detect, investigate and resolve financial data errors
Handle point-in-time accuracy and survivorship bias in financial datasets
Resolve data integrity issues like conflicting prices, corporate actions and security IDs
Build scalable pipelines to integrate new financial datasets
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.
Build reliable autonomous agents that detect, investigate and resolve financial data errors, knowing when to escalate to humans.
Build reliable autonomous agents that detect, investigate and resolve financial data errors
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.
You have experience with Bloomberg, S&P Capital IQ, FactSet, LSEG/Refinitiv, MSCI Barra, or similar data providers.
Experience with Snowflake, Databricks, Airflow, AWS, or related technologies.
From the employer’s posting.
Whoever builds the leading intelligence for finance will create far more than returns.
In the employer’s words · Read in context · Company website ↗
Actively hiringFirst seen by us 5 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
$127K–$190K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 2,133 postings in the last year. Stated ranges, not salaries paid.
Day 5
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From 10,736 similar postings that came down. Coming down is not always a hire.
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Nice to Have - Experience with Snowflake, Databricks, Airflow, AWS, or related technologies. Location
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We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
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.
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.
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.
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.
Experience with Snowflake, Databricks, Airflow, AWS, or related technologies.
Hybrid, 3 days per week on-site at our office in Menlo Park, CA. Relocation allowance available.
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.
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.
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Menlo Park, United States
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