Data Engineer

Charles Schwab Inc.•Southlake, TX

1d•$53 - $60•Onsite

About The Position

Schwab is seeking a results-oriented Senior Data Engineering professional to join the Operational Data Exchange (ODX) Technology team within Schwab Data. The team delivers operational intelligence and enterprise data solutions supporting key business functions across Schwab, primarily Schwab Technology Services (STS). The role advances Schwab’s data-driven transformation by developing scalable, high-performing data solutions on a modern cloud-based data platform. The Data Engineer partners with engineers, technical leads, analysts, and business stakeholders to design, build, and optimize data pipelines that provide trusted, actionable enterprise insights. The work supports system stability, faster time to market, operational efficiency, and informed business decisions. This hands-on position uses cloud technologies, data integration tools, and software engineering best practices to create reliable end-to-end data solutions. Responsibilities span the development lifecycle, from analyzing business requirements and translating them into technical designs to implementing data quality controls, supporting production deployments, and driving continuous improvement. The role calls for problem-solving, adaptability, global collaboration, and the ability to influence outcomes through data-driven recommendations. The selected candidate is expected to work onsite in the specified location(s). The position is eligible for bonus or incentive opportunities in addition to the salary range.

Requirements

Bachelor’s degree in Computer Science, Information Technology, or a related technical field.
At least 3 years of data engineering, ETL development, or data integration experience.
At least 3 years of experience designing, developing, and supporting enterprise data pipelines.
Strong experience with SQL and relational database technologies.
Hands-on experience with Google Cloud Platform, including BigQuery, Cloud Storage, Dataflow, Composer, and Pub/Sub.
Python development experience for data engineering and automation solutions.
Experience developing and supporting ETL processes and enterprise data integration solutions, including Informatica Intelligent Cloud Services (IICS).
Experience implementing data quality, validation, exception handling, and data governance best practices.
Experience gathering, analyzing, and translating business requirements into technical solutions and source-to-target data mappings.
Ability to design and optimize solutions for processing large volumes of structured and unstructured data.
Experience working within Agile software development environments.
Strong problem-solving, analytical, and decision-making skills.
Ability to collaborate effectively with cross-functional teams and communicate technical concepts to diverse audiences.
Ability to mentor junior team members and contribute to team success.
Commitment to continuous learning and adoption of emerging technologies.

Nice To Haves

Master’s degree.
Experience supporting enterprise platforms such as Workday, BMC Remedy Helix, Jira, Rally, or similar business systems.
Experience with DevOps and software delivery tools including GitHub, Bamboo, and Liquibase.
Strong understanding of data architecture principles, development standards, and engineering best practices.
Experience creating and maintaining source-to-target mapping documentation.
Ability to identify operational challenges, recommend solutions, and drive process improvements.
Strong written and verbal communication skills, including technical documentation.
Experience working within highly collaborative, fast-paced, and evolving technology organizations.

Responsibilities

Develop scalable, high-performing data solutions on a modern cloud-based data platform.
Partner with engineers, technical leads, analysts, and business stakeholders to design, build, and optimize data pipelines.
Analyze business requirements and translate them into technical designs and source-to-target data mappings.
Develop and support enterprise data pipelines, ETL processes, and data integration solutions.
Implement data quality controls, validation, exception handling, and data governance practices.
Design and optimize solutions for processing large volumes of structured and unstructured data.
Support production deployments and drive continuous improvement initiatives.
Mentor junior team members and contribute to team success.
Identify operational challenges, recommend solutions, and drive process improvements.