Senior Data Engineer

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.

Wealth and Asset Management (WAM) Engineering is an organization within Schwab Technology Services aligned to support the technology needs of Schwab Asset Management (SAM), Wealth & Advice Solutions (WAS), the Schwab Center for Financial Research (SCFR), and Third-Party Platform teams. Wealth Management Engineering (WME), an organization within WAM Engineering, is dedicated to providing support to WAS which is a growth area for Schwab.

This newly created role will support the future growth of the WAS business. As a seasoned, hands‑on Senior Data Engineer, you will enable WME to achieve consistent, predictable, high‑quality delivery by implementing best practices, tools, metrics, automation frameworks, and providing oversight in the areas of software development and testing. You’ll serve as a strong and versatile hands‑on technical engineer. You will deliver high‑quality solutions that meet business objectives in a flexible, collaborative, and rapidly changing environment.

You will play a critical role in supporting key stakeholders across the WAS organization which is focused on high‑net worth and ultra‑high net worth retail investors. Supported business functions span Schwab’s Wealth and Investment Solutions including Schwab Wealth Advisory. You will be working with a team of talented and highly motivated technologists that strive to make technology a strategic differentiator for Schwab’s WAS business and their clients.

Define and maintain conceptual, logical, and physical database models by working with business stakeholders and identifying optimal database specifications. Ensures that database design and system capabilities meet validated user requirements. Work with application development teams designing, developing, and enhancing database schemas, creating database views, helping with query optimization, and figuring out ways to increase application performance. Enhance and recommend solutions/processes for downstream impact analysis associated with data and/or model changes.

Support, maintain and enhance applications (when necessary) written with ETL tools such as Informatica, Airflow, Snowflake and additionally support teams using custom ingestion and transformation processes. Collaborate with enterprise stakeholders to implement and adhere to data storage standards and maintain catalogs to facilitate consistent data management practices. Establish data quality metrics and monitoring processes to proactively identify and resolve data quality issues.

Work with various IT departments within Schwab to perform preventive and corrective maintenance and incident management measures. Ensure database integrity, availability, and restorability by working with enterprise operational excellence teams. Evaluates the integration of new tools or technologies based on the capabilities of the existing infrastructure. Recommend solutions and help with functional and performance testing needs (mock data, test environments, data refresh processes).

Responsible for coordination of planning the installation of operating systems, databases, applications, development upgrades, and new releases. Follow and develop database standards, guidelines, and best practices. Establishes standards, controls, and procedures to ensure data integrity and security. Perform capacity planning and projection for future growth. And finally, be able to work in an Agile Scrume environment.

• Bachelor of Science in Computer Science or a related field
• The ideal candidate should have at least 7+ years of data engineering experience, 5+ years development experience and 3 years or more experience with Postgres database management. The candidate must additionally have experience with Oracle, SQL Server, MySQL, and/or Mongo DB.
• Must have worked as alongside a business vertical to establish nomenclature, data catalogs, provenance, standards, semantics and usage of data.
• Deep knowledge and…