> Markdown version of [/jobs/ext/1071292-data-engineer-quantitative-research](https://www.wearedevelopers.com/jobs/ext/1071292-data-engineer-quantitative-research). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer, Quantitative Research - **Company:** TD Ameritrade - **Location:** United States - **Experience:** Experienced - **Salary:** $83,800.0 - $140,000.0 - **Contract:** Permanent contract - **Skills:** JavaScript (Programming Language), Airflow, C++ (Programming Language), Continuous Integration, Data Architecture, Data Validation, Information Engineering, Data Governance, Data Infrastructure, Data Visualization, Iterative and Incremental Development, Python (Programming Language), Meta-Data Management, Software Engineering, SQL Databases, Workflow Management Systems, Rust (Programming Language), Git, Pandas, Information Technology, Machine Learning Operations - **Published:** June 13, 2026 - **Apply:** https://www.schwabjobs.com/job/lone-tree/data-engineer-quantitative-research/33727/93187525152 ## About the Role * 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). ## Description 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [Getting to Know Your Legacy (System) with AI-Driven Software Archeology](https://www.wearedevelopers.com/videos/1437-getting-to-know-your-legacy-system-with-ai-driven-software-archeology) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Data Analyst Salary in the UK](https://www.wearedevelopers.com/magazine/278-data-analyst-salary-in-the-uk) - [The Most Popular IT Jobs on the Market](https://www.wearedevelopers.com/magazine/376-the-most-popular-it-jobs-on-the-market) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)