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About the Role

The Manager, Data Engineering is an individual contributor role within the Offer Development, Delivery and Analytics department, supporting business-unit data assets, data mart solutions, reporting, and analytics. This role focuses on delivering reliable, scalable data solutions, improving data quality, platform performance, and engineering practices. Responsibilities include defining requirements, developing ETL/ELT logic, testing, and providing operational support. The ideal candidate is a hands-on data engineer with strong technical and business acumen, motivated to learn corporate systems, reinforce engineering best practices, and contribute to a future-state data platform.

Responsibilities

Build and maintain data mart solutions that support reporting and analytics use cases.
Design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data.
Develop and troubleshoot ETL/ELT logic using SQL and team tooling.
Design and build dimensional data models, including facts and dimensions, determine appropriate table grain, and implement slowly changing dimensions where historical tracking is required.
Define and implement practical data retention and history strategies that preserve analytical value without overloading downstream reporting tools.
Implement and maintain data quality controls, reconciliation checks, testing, and monitoring to ensure data accuracy, consistency, and reliability.
Support production reliability through job monitoring, issue resolution, root-cause analysis, operational support, and documentation.
Create and maintain production support and deployment artifacts.
Collaborate with business stakeholders and technical teams to translate business needs into scalable technical solutions, including metric logic, and data definitions.
Work closely with development partners, product owners, and team members to design features, decompose stories, and prioritize delivery.
Share technical knowledge and support team success through collaboration, documentation, and guidance.
Provide technical leadership for data pipeline development and engineering practices.
Navigate cross-functional communication effectively to maintain alignment across teams.
Use data-driven reasoning to constructively challenge decisions, align on outcomes, and execute once direction is set.
Identify technology risks and dependencies early and help establish mitigation plans.
Implement data security, governance, and metadata management practices to protect sensitive information.
Contribute to a culture of open feedback, accountability, and continuous improvement.

Requirements

Expertise in ETL/ELT development, SQL, and data engineering best quality practices including data quality, testing, monitoring, and exception handling.
Strong understanding of data pipelines, data mart design, and common engineering patterns.
Strong understanding of data warehouse concepts, including star schema, fact and dimension modeling, table grain, slowly changing dimensions, and operational data stores.
Experience with Google Cloud technologies, including BigQuery and Cloud Storage.
Business analysis experience to translate business requirements into data mappings, metric logic, and data definitions, and to perform data analysis.
Solid understanding of the data lifecycle, metadata management, and governance standards.
Ability to recommend practical data retention and history strategies that balance analytical value with reporting performance.
Strong cross-functional collaboration skills with leadership, colleagues, and stakeholders.
Strong communication and stakeholder management skills across technical and non-technical audiences.
Willingness to learn new skills and adapt to evolving technologies to meet future business needs.
Proficiency with development tools including version control (for example, GitHub), project management software (for example, JIRA), and orchestration tools (for example, Control-M, SQL Server Integration Services, Informatica, or similar).
Experience leveraging AI in development lifecycle, and enabling AI-ready data environments.

Skills

ETL/ELT development
SQL
Data quality practices
Data testing
Data monitoring
Exception handling
Data pipelines
Data mart design
Data warehouse concepts
Star schema
Fact and dimension modeling
Table grain
Slowly changing dimensions
Operational data stores
Google Cloud technologies
BigQuery
Cloud Storage
Business analysis
Data analysis
Data lifecycle
Metadata management
Governance standards
Cross-functional collaboration
Communication
Stakeholder management
Version control
GitHub
Project management software
JIRA
Orchestration tools
Control-M
SQL Server Integration Services
Informatica
Power BI
Tableau
Python
AI in development lifecycle
AI-ready data environments

Location

On site in the specified location(s)

Work Type

On site

Experience Level

Minimum of 3 years of hands-on data engineering experience
5+ years of experience with reporting and data visualization tools
5+ years of experience with data management tools and coding languages
3+ years of experience in the financial services industry and/or a B2B environment

Education Level

Bachelor’s or master’s degree in computer science, information technology, or a related field
Equivalent practical experience

About the Company

Schwab empowers employees to make an impact, fostering innovative thought and creative problem solving to transform the finance industry.
Schwab Advisor Services is a division of Charles Schwab & Co., Inc.
Schwab Advisor Services is the leading provider of custody, trading, technology, and practice management to registered investment advisors (RIAs).
Schwab Advisor Services serves over 16,000 independent advisory firms.
Over $5 trillion of assets are custodied with Schwab by independent advisory firms.