Data Engineer, Quantitative Research

Your opportunity

At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together. 

We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified locations.

The Schwab Center for Financial Research (SCFR) is at the center of advice for Charles Schwab & Co., Inc. (CS&Co). We lead all aspects of the selection of investments and ongoing monitoring of the Mutual Fund OneSource Select List®, Managed Account Select®, ETF Select List®, Schwab Personalized Portfolio Builder, product market commentary, and other investment advice for our clients and financial consultants. We are published in respected business and academic journals and frequently cited by the media on investment topics! SCFR is distinguished by our ability to effectively combine both meticulous quantitative work with manager profiling and market analysis in our research.

This position sits at the intersection of technology and research. You’ll support the quantitative investment due diligence process for the selection of mutual funds, ETFs, and alternative investments for Schwab clients. Day to day, you’ll build and maintain the data infrastructure behind proprietary models, run production analyses, and collaborate directly with our research team. In addition to a competitive salary, this role is eligible for bonus and incentive opportunities.

You’ll work on a small, high-impact team with real ownership over the tools and systems you build. We operate with a bias toward pragmatic solutions, building what matters with the tools at hand, iterating quickly, and making the most of every investment in infrastructure and process. Your responsibilities will span data engineering, production support, and infrastructure work. Primary responsibilities include:

• Production systems and pipelines: maintain, design and build data acquisition, staging, cleaning, and transformation pipelines; support model production processes, with an emphasis on streamlining data review, output validation, and other manual workflows; troubleshoot and resolve production issues; ensure production processes are well-documented and repeatable.
• Data architecture and frameworks: build and maintain the team’s lakehouse platform; develop data onboarding, schema, validation, and observability frameworks; support migration to modern data platforms and tools; identify and implement process improvements to enhance reliability, efficiency, and controls.
• Cross-team technology coordination: coordinate with technology teams on infrastructure, integration, and access requirements; participate in cross-functional discussions on platform direction and tooling standards.
• Research collaboration: partner with the research team to support quantitative investment due diligence efforts; contribute to research methodology and tool improvements over time.

What you have

Required qualifications:

• A bachelor’s degree in Financial Engineering, Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• 2+ years of relevant experience in financial data management or data engineering; or an advanced degree in a quantitative field.
• Proficiency in Python (Pandas, Polars), SQL, and Git.
• Experience cleaning, transforming, or validating data.
• Experience building automated or reproducible workflows.
• Clear written and verbal communication skills.

Preferred qualifications:

• Experience with lakehouse architecture or building and maintaining modern data platforms (strong plus).
• Experience with software development lifecycle practices (e.g., CI/CD, TDD) (strong plus).
• Experience building agent harnesses or developing AI-assisted development workflows (strong plus).
• Experience with financial data providers such as Bloomberg or Morningstar.
• Experience in data visualization using Python-based or web-native tools.
• Demonstrated ability to communicate technical findings to non-technical stakeholders.
• Familiarity with agile or iterative development workflows and project tracking tools.
• Additional experience that will set you apart:
  • MLOps workflows, model lifecycle management, or experiment tracking frameworks (e.g., MLflow, DVC).
  • Languages common in modern data toolchains (JavaScript, Rust, C++).
  • Workflow orchestration tools (e.g., Dagster, Airflow).
  • Data governance or data cataloging and observability tools (e.g., OpenMetadata).

In addition to the salary range, this role is also eligible for bonus or incentive opportunities.