Quantitative Software Engineer - Research Platform

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 location(s).

Schwab Technology Services enables the future of how clients manage their money by delivering innovative and reliable technology solutions that support investing and financial planning. Within Schwab Asset Management Technology, this role helps power the research, analytics, and investment capabilities that support clients, advisors, and investment professionals.

As a Quantitative Software Engineer - Research Platform, you will help shape Schwab’s research technology ecosystem by building scalable data, analytics, and platform capabilities that accelerate quantitative research and product innovation. Working closely with researchers, engineers, product owners, and business partners, you will solve complex data and technology challenges, enable data-driven decision-making, and create reusable solutions that improve efficiency, speed to market, and platform adoption across investment disciplines. This role offers the opportunity to influence strategic platform direction while delivering technology that supports investment research and actionable client insights. What you have Required Qualifications:

Bachelor’s degree in Computer Science, Information Systems, Mathematics, Engineering, or a related technical discipline, or equivalent practical experience. 6+ years of software engineering experience developing data-intensive applications, analytical platforms, or quantitative systems using Python and/or similar languages such as R, MATLAB, or Julia. Experience partnering with researchers, analysts, product owners, or business stakeholders to deliver data-driven technology solutions. Experience designing, developing, and supporting scalable data pipelines and data platforms for structured and unstructured datasets. Proficiency designing and implementing data models for efficient storage, integration, retrieval, and analysis. Experience implementing data quality controls, monitoring, governance, lineage, and observability practices. Experience developing software capabilities that support quantitative research, analytics, or other data-intensive workloads. Experience applying modern software engineering and CI/CD practices, including source control, automated testing, containerization, and deployment automation. Experience building automated testing frameworks for integration, regression, and data validation testing. Experience developing data visualizations, dashboards, and analytical tools that enable actionable insights. Experience collaborating within Agile and DevOps environments across engineering, product, architecture, and governance teams.

Advanced degree in Computer Science, Engineering, Mathematics, Quantitative Finance, or a related technical discipline. Experience with cloud-native data platforms and architectures; Snowflake and/or Google Cloud Platform (GCP) experience preferred. Experience building distributed data processing, streaming, or large-scale analytics solutions. Experience developing reusable platforms, frameworks, libraries, or shared services. Experience supporting quantitative investment research, model development, portfolio analytics, backtesting, or investment management workflows. Experience analyzing large, complex datasets to identify meaningful insights and opportunities. Experience developing self-service analytics, visualization, and researcher productivity tools. Experience implementing model lifecycle management, monitoring, observability, and risk-control frameworks. Experience applying machine learning, generative AI, or advanced analytics