Software Engineer- Investment Research and Data

Charles Schwab Inc.•San Francisco, CA

7d•$50 - $60

About The Position

The Schwab Asset Management (SAM) Engineering organization is a part of Schwab Technology Services that is responsible for Schwab's investment management, operations, data, and research platforms. Everything that SAM Engineering builds serves Schwab’s mission of helping clients reach their financial goals. As a Software Engineer in the SAM Engineering Investment Research Technology group, you will work as a hands-on technologist focused on our quantitative research initiatives. We currently support a variety of existing on-premises and cloud solutions, and we are actively expanding the capabilities of these platforms to drive cutting-edge research and product development activities. We partner closely with researchers and product teams to acquire, curate, and operationalize diverse datasets — so researchers can move from ideas to back tested signals and production-ready models faster, with strong governance and quality. In this role, you’ll join a collaborative engineering team and contribute hands-on to our modern data platform: building and improving data pipelines, implementing automated data-quality checks, and helping deliver production-grade datasets and services used across research workflows. It’s a great opportunity to grow your software engineering and data engineering skills while seeing your work directly enables quantitative research and model development.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field (or equivalent practical experience).
0–2 years of software engineering experience (including internships, co-ops, undergraduate research, or substantial project work).
Proficiency in at least one general-purpose programming language (e.g., Python, Java, C#, or similar) and comfort learning new technologies quickly.
Working knowledge of data fundamentals: relational data concepts, writing SQL queries, and designing/debugging ETL/ELT-style data transformations.
Understanding of core software engineering practices such as version control (Git), code review, and automated testing concepts.
Ability to troubleshoot issues using logs/metrics and to communicate clearly with teammates and stakeholders about progress, risks, and next steps.
Demonstrated analytical/problem-solving skills, including the ability to break down ambiguous problems into smaller, testable steps.
Commitment to secure development and responsible data handling (e.g., least privilege, secrets management, and following data governance standards).

Nice To Haves

Experience building and supporting data pipelines using common patterns/tools (e.g., Airflow or similar orchestrators; dbt or similar transformation tooling).
Expertise building data visualization capabilities using tools such as Plotly Dash, Streamlit, etc.
Familiarity with cloud services and concepts (compute, storage, IAM), and/or experience running workloads in a cloud environment.
Exposure to containerization and deployment tools (Docker; Kubernetes or similar) and CI/CD pipelines (e.g., GitHub Actions, Azure DevOps, Jenkins).
Experience with observability practices (logging, metrics, alerting) and on-call/production support concepts.
Familiarity with data modeling concepts and modern data stores (e.g., columnar formats such as Parquet, data lakes/warehouses such as Snowflake, time-series data).
Experience working with large or messy real-world datasets and implementing data validation/testing (e.g., Great Expectations or similar).
Interest in quantitative finance, statistics, or machine learning, and enthusiasm for enabling research teams with reliable data and tools.
Strong written communication skills (documentation, runbooks, design notes) and a collaborative approach to working across disciplines.

Responsibilities

Partner with researchers, product owners, and other engineers to clarify requirements, ask the right questions, and translate business needs into well-scoped technical tasks and deliverables.
Build and enhance data pipelines to ingest, transform, validate, and publish datasets used for quantitative research and downstream analytics.
Implement data-quality controls (e.g., schema checks, completeness/accuracy rules, anomaly detection) and contribute to data lineage, documentation, and operational runbooks.
Contribute to our data platform and supporting services (APIs, shared libraries, workflow orchestration, scheduling), with an emphasis on maintainability, performance, and reliability.
Write clean, testable code and practice disciplined engineering: unit/integration tests, code reviews, version control, and adherence to Schwab development standards.
Collaborate with DevOps, production support, and partner technology teams to deliver supportable solutions, including CI/CD, monitoring/alerting, and day-2 operational readiness.
Participate in Agile ceremonies (standups, grooming, sprint planning, demos, retros) and communicate progress, risks, and dependencies clearly and early.
Support incident triage and problem management by analyzing logs/metrics, identifying root causes, and driving fixes to reduce recurrence (with mentorship as needed).
Apply security and compliance best practices (least privilege, secrets handling, secure coding) and follow data governance guidelines when handling sensitive information.
Leverage modern development tools—including AI-assisted coding tools where appropriate—to accelerate delivery while maintaining high quality, correctness, and proper review practices.