Data profiling & analysis: Use SQL and Python to explore large vendor datasets, identify anomalies and edge cases, and document data patterns, assumptions, and gaps.
Data development: Design, build, and maintain simpler to moderately complex data pipelines and parsers to transform and load XML, JSON, TXT, and CSV data into canonical database models.
Requirements & mapping: Partner with product and business partners to create clear source-to-target mappings, data definitions, and transformation rules that enable consistent downstream development.
Quality assurance: Develop automated validation checks and tests to confirm data accuracy, completeness, and consistency; support monitoring and continuous improvement of pipeline quality.
Collaboration & delivery: Communicate findings and recommendations to technical and non-technical stakeholders, and contribute to team standards for documentation, readability, and maintainability.
What you have
Experience: 1-3 years of experience in a data engineering, data analytics, or software development role focused on data-intensive work.
SQL proficiency: Ability to write complex joins, window functions, aggregations, and investigative queries to reconcile discrepancies and validate results.
Programming skills: Proficiency in Python (or a similar language) for data wrangling, file parsing, and automation; experience using common data manipulation libraries (for example, pandas or equivalent).
Data formats: Hands-on experience working with semi-structured and flat-file data (XML, JSON, CSV, TXT), including transformation, validation, and loading patterns.
Analytical mindset: Ability to investigate "why" behind the data, form hypotheses, and translate findings into clear, actionable technical direction.
Communication & documentation: Ability to clearly document logic, mapping rules, and decisions, and explain technical findings to partners with varied technical backgrounds.
Requirements
Exposure to financial services datasets (for example, market data, credit/ratings, or asset management data concepts).
Familiarity with modern data and cloud ecosystems (such as AWS, GCP, Snowflake, Databricks) and development practices for building reliable data pipelines.
Experience with version control and collaborative engineering workflows (Git), including code reviews and basic testing practices.
Interest in using AI-assisted development tools to accelerate routine work while maintaining quality, security, and sound engineering judgment.
Bachelor's degree or equivalent practical experience in computer science, engineering, analytics, or a related field.
In addition to the salary range, this role is also eligible for bonus or incentive opportunities
Additional Information
Pay range: USD $45.67 - $52.88 / Hour
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.
Schwab Technology Services enables the future of how clients manage their money by providing innovative and reliable technology products and services as a part of our ongoing commitment to democratize access to investing and financial planning.
We are seeking an analytical, data-driven engineer to join our Financial Data Engineering team, where you will help transform third-party financial datasets into trusted, usable data products that power downstream engineering and analytics. In this hybrid role, you will start with the data-profiling vendor feeds, identifying anomalies, and partnering with business and product stakeholders to define clear data mappings-then build Python- and SQL-based parsers and pipelines that load data into canonical models. You will collaborate across teams to improve data quality, automate validation, and reduce time-to-insight while maintaining high standards for reliability and accuracy. We encourage applicants with varied experiences and career paths who bring curiosity, strong problem-solving skills, and a commitment to building dependable data foundations.
Skills you will build in this role
Translating complex vendor data into well-defined source-to-target mappings and business rules
Designing maintainable parsers and data pipelines for semi-structured and flat-file formats
Implementing automated data quality checks and validation patterns that scale
Strengthening communication and documentation skills for cross-functional engineering work
Charles Schwab · Austin, TX