01Key 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 toolsincluding AI-assisted coding tools where appropriateto accelerate delivery while maintaining high quality, correctness, and proper review practices.
Required Qualifications:
Bachelors degree in Computer Science, Engineering, Mathematics, Statistics, or a related field (or equivalent practical experience).
02 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 .
