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About the Role

The Sr. Manager, Analytics Engineering and Data Strategy will play a key role in establishing future-ready workforce data foundations that power analytics, AI, reporting, and intelligent decision-making across ETS. The role sits at the intersection of business strategy, analytics engineering, data architecture, and data products, translating complex business and metric needs into trusted, scalable, and reusable data capabilities.

Responsibilities

Lead the design and development of trusted, analytics-ready data foundations that transform complex enterprise and workforce data into reusable, business-ready datasets
Lead discovery across complex or fragmented workforce data domains to understand source systems, business definitions, data grain, keys, history, dependencies, quality considerations, and downstream consumption needs
Design scalable data models, transformation layers, and architectures that support workforce metrics, reporting, advanced analytics, and AI
Establish consistent approaches to business rules, historical data, reusable dimensions, data quality, and lineage
Partner with business and technical stakeholders to translate analytical needs into scalable, reusable data products rather than one-off solutions
Define data-product architecture, transformations, business logic, quality standards, documentation, and ownership
Balance immediate business priorities with long-term architectural scalability, maintainability, and reuse
Lead hands-on development of complex data transformations using SQL, BigQuery, and Python, leveraging tools such as dbt, Git, and Apache Airflow or comparable technologies
Establish engineering standards for modular development, version control, testing, deployment, orchestration, and monitoring
Implement automated data-quality, validation, reconciliation, and lineage capabilities to improve trust and reliability
Serve as a technical bridge across Business, Analytics, Data Engineering, Architecture, Governance, and enterprise technology teams
Shape the analytics engineering and workforce data strategy, identifying opportunities to simplify fragmented data ecosystems and build scalable capabilities
Enable reliable data consumption across Power BI, Excel, SQL, Python, and emerging AI solutions
Mentor team members and promote engineering practices focused on reusability, automation, quality, and business value
Mentor and upskill analysts and other team members in SQL, data modeling, data-product thinking, reusable transformation patterns, data quality, and modern analytical development practices
Promote a data product mindset centered on business value, reliability, reusability, maintainability, automation, quality, and ownership

Requirements

Bachelor's degree in Computer Science, Computer Engineering, Data Science, Information Systems, Business Analytics, or a related field preferred; advanced degree or equivalent experience welcomed
Strong understanding of modern analytics engineering, data architecture, data modeling, and data-product principles.
Advanced knowledge of SQL and strong understanding of cloud data platforms and modern enterprise data ecosystems.
Strong understanding of dimensional modeling, fact and dimension design, data grain, keys, historical data, slowly changing dimensions, analytical marts, and downstream consumption patterns.
Knowledge of modern transformation and orchestration approaches, including SQL-based transformation frameworks, dbt or comparable technologies, Apache Airflow or comparable orchestration platforms, and Python.
Understanding of software-engineering practices applied to data development, including Git, peer review, automated testing, CI/CD concepts, reusable code, environment management, and controlled deployment.
Knowledge of data governance, lineage, metadata, data quality, ownership, privacy, and enterprise data-management principles.
Understanding business intelligence and analytical consumption patterns, including Power BI and other analytical tools, and how upstream data architecture affects downstream semantic models, metrics, reporting, and analytics.
Understanding of how trusted, reusable data products can enable AI, advanced analytics, intelligent decision-making, and emerging data-driven capabilities
Advanced knowledge of AI-ready enterprise data architecture and scalable data platform design patterns
Advanced hands-on SQL experience, including complex transformations, dimensional modeling, analytical marts, and production-grade reusable datasets.
Experience working with cloud data warehouses, with Google BigQuery experience strongly preferred
Experience using Python for data processing, automation, validation, reconciliation, or related data-engineering use cases
Experience translating complex or ambiguous business problems and metric needs into scalable data architecture, models, transformations, and reusable data solutions
Significant experience designing analytics-ready data foundations and governed enterprise data products, including ownership, quality, lineage, and documentation
Ability to independently navigate fragmented data environments, investigate unfamiliar data domains, and lead scalable solutions amid evolving requirements
Experience applying modern analytics engineering practices, including version control, automated testing, code review, documentation, deployment, and monitoring
Experience with dbt or comparable SQL-based transformation frameworks and Apache Airflow or similar orchestration technologies preferred
Experience collaborating across Business, Analytics, Data Engineering, Architecture, Data Governance, and Technology teams to deliver enterprise data solutions
Demonstrated ability to balance immediate delivery priorities with long-term architecture, reusability, governance, and maintainability
Strong communication skills, including the ability to explain technical concepts, architecture decisions, and trade-offs to nontechnical stakeholders
Experience mentoring data practitioners and advancing scalable analytics engineering and data-product practices
Experience leading implementation of AI-enabled data platform modernization or operational transformation initiatives
Experience designing scalable data infrastructure supporting advanced analytics, ML, or GenAI workloads
Experience establishing governance and quality standards for AI-consumable enterprise data assets

Skills

SQL
BigQuery
Python
dbt
Git
Apache Airflow
Power BI
Excel

Location

Work Type

Hybrid
Onsite
Virtual

Experience Level

Sr. Manager
Advanced

Education Level

Bachelor's degree

Benefits

Competitive base salaries
Bonus incentives
6% Company Match on retirement savings plan
Free financial coaching and financial well-being support
Comprehensive medical, dental, vision, life insurance, and disability benefits
Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy
Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
Free and confidential counseling support through our Healthy Minds program
Career development and training opportunities

About the Company

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues.
From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.
As part of Team Amex, you’ll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career.
Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.

Equal Opportunity

American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law.
American Express will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable state and local laws, including the California Fair Chance Act, the Los Angeles County Fair Chance Ordinance for Employers, and the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance.
For positions covered by federal and/or state banking regulations, American Express will comply with such regulations as it relates to the consideration of applicants with criminal convictions.
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