Senior Manager, Analytics Engineering and Data Strategy - Technology Business Enablement

American Express•New York, NY

About The Position

The Senior Manager, Analytics Engineering and Data Strategy will establish future-ready workforce data foundations that support analytics, AI, reporting, and intelligent decision-making across Enterprise Technology Services (ETS). The role combines business strategy, analytics engineering, and data architecture to translate business and metric needs into trusted, scalable, reusable data capabilities. Working with business and technology stakeholders, this leader will discover and assess data, design data and transformation architectures, and build reusable analytics data products for multiple workforce use cases. The position will help move Workforce Strategy from fragmented, report-specific data preparation toward a governed analytics ecosystem in which critical data and business logic are reusable, trusted, documented, and engineered for scale. The role leads analytics data foundation and data-product development from discovery through architecture, engineering, adoption, and ongoing ownership. It also establishes modern analytics engineering practices and development standards to improve reliability, quality, maintainability, and speed, while enabling analysts and business teams to focus more on insights and outcomes. The position collaborates across Business, Analytics, Data Engineering, Architecture, Data Governance, and Technology teams, and mentors colleagues in reusable, high-quality data practices.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Data Science, Information Systems, Business Analytics, or a related field is preferred; equivalent experience is 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 tools, Apache Airflow or comparable platforms, and Python.
Understanding of software-engineering practices for 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 of business intelligence and analytical consumption patterns, including Power BI, and how upstream architecture affects 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.
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 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.

Nice To Haves

Advanced degree or equivalent experience.
Google BigQuery experience.
Experience with dbt or comparable SQL-based transformation frameworks.
Experience with Apache Airflow or similar orchestration technologies.
Experience leading implementation of AI-enabled data platform modernization or operational transformation initiatives.
Experience designing scalable data infrastructure for advanced analytics, ML, or GenAI workloads.
Experience establishing governance and quality standards for AI-consumable enterprise data assets.

Responsibilities

Lead the design and development of trusted, analytics-ready foundations that transform enterprise and workforce data into reusable, business-ready datasets.
Discover complex or fragmented workforce data domains, including source systems, definitions, data grain, keys, history, dependencies, quality considerations, and downstream needs.
Design scalable data models, transformation layers, and architectures for workforce metrics, reporting, advanced analytics, and AI.
Establish consistent approaches to business rules, historical data, reusable dimensions, data quality, and lineage.
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, with 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.
Serve as a technical bridge across Business, Analytics, Data Engineering, Architecture, Governance, and enterprise technology teams.
Shape analytics engineering and workforce data strategy by 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 and upskill analysts and team members in SQL, data modeling, data-product thinking, reusable transformation patterns, data quality, and modern analytical development.
Promote data products centered on business value, reliability, reusability, maintainability, automation, quality, and ownership.