Quantitative Software Engineer - Research Platform
About The Position
At Schwab, the Quantitative Software Engineer - Research Platform helps shape Schwab’s research technology ecosystem by building scalable data, analytics, and platform capabilities that accelerate quantitative research and product innovation. The role works closely with researchers, engineers, product owners, and business partners to address 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. The position offers the opportunity to influence strategic platform direction while delivering technology that supports investment research and actionable client insights. It is part of Schwab Asset Management Technology, within Schwab Technology Services, which delivers technology solutions supporting investing and financial planning. The selected candidate is expected to work on site in the specified location(s). The role is eligible for bonus or incentive opportunities in addition to the salary range.
Requirements
- Bachelor’s degree in Computer Science, Information Systems, Mathematics, Engineering, or a related technical discipline, or equivalent practical experience.
- 6 or more 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.
Nice To Haves
- 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 capabilities to business problems.
- Ability to influence technology strategy, architecture decisions, engineering standards, and platform direction.
- Strong verbal and written communication skills with technical and non-technical audiences.
- Demonstrated commitment to innovation, experimentation, and continuous improvement.
Responsibilities
- Build scalable data, analytics, and platform capabilities that accelerate quantitative research and product innovation.
- Partner with researchers, engineers, product owners, and business partners to 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.
- Develop and support data pipelines and platforms for structured and unstructured datasets.
- Design and implement data models for efficient storage, integration, retrieval, and analysis.
- Implement data quality controls, monitoring, governance, lineage, and observability practices.
- Develop software capabilities that support quantitative research, analytics, and other data-intensive workloads.
- Apply modern software engineering and CI/CD practices, including source control, automated testing, containerization, and deployment automation.
- Build automated frameworks for integration, regression, and data validation testing.
- Develop data visualizations, dashboards, and analytical tools that enable actionable insights.
- Collaborate across engineering, product, architecture, and governance teams in Agile and DevOps environments.
Benefits
- Eligible for bonus or incentive opportunities in addition to the salary range.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
