Your opportunity

Schwab Description:

​​At Charles Schwab, our purpose is simple: we champion client’s goals with passion and integrity. Guided by honesty, mutual respect and a commitment to doing what’s right, we bring innovation, education, and service together to help shape financial futures. Our people are the foundation of our success – they approach their work with curiosity and collaboration, coming together to create solutions that make a meaningful impact for clients and communities. As we expand into India, we are bringing this same culture of inclusion, learning, and opportunity to new talent. Joining us means becoming part of a global team where your work matters and your future can take shape.​

Schwab India​:

Our Hyderabad location is central to Schwab’s growth, bringing together talented people and technology to drive innovation, scale and efficiency. Here, you will work alongside teams who create solutions that support millions of clients every day. The work you do is more than daily operations – it’s a chance to experiment, learn, and build within a values-driven, supportive environment. This is a unique opportunity to be part of our early growth phase and shape something new, backed by the stability and strength of a Fortune 500 company. Your impact begins on day one, and your contributions will help define our future in the region​

What you have

Role Description:

​As a Software Engineer supporting the Schwab Asset Management (SAM) Engineering Investment Research Technology group, you will work as a hands-on technologist focused on our quantitative research initiatives. We currently maintain a variety of existing on-premise 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 backtested 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 enable quantitative research and model development.

Key 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.

Required Qualifications:

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).

Preferred Qualifications:

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.