Senior Manager, Analytics Engineering, Data Strategy

American Express

. Lead the design and development of trusted, analytics-ready data foundations transforming enterprise and workforce data into reusable, business-ready datasets .

Posted 11/3/2026full-timeNew York City • New York • United StatesSenior💰 $123,000 - $215,250 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and developing analytics-ready data foundations, with advanced skills in SQL, Python, and cloud data platforms like Google BigQuery. Proficient in data modeling, transformation frameworks, and establishing data governance and quality standards for enterprise data products.

Highest-signal resume keywords

Advanced SQL ExperienceData Architecture DesignCloud Data PlatformsAnalytics Engineering PracticesData Governance and Quality Standards

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Applicant Tracking System Keywords

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Hard Skills

SQLPythonData ModelingDimensional ModelingData TransformationAnalytics EngineeringData GovernanceData QualityAI-Ready Data ArchitectureData Product Principles

Soft Skills

Strong Communication SkillsMentoring and UpskillingCollaboration Across Teams

Tools & Technologies

Google BigQueryDbtApache AirflowPower BIExcelCI/CD ConceptsVersion Control

Industry Keywords

Enterprise Data ManagementData EcosystemsBusiness IntelligenceAdvanced AnalyticsData Lineage

Tech Stack

Tools & technologies

AirflowApacheBigQueryCloudPythonSQL

About the role

Key responsibilities & impact

Lead the design and development of trusted, analytics-ready data foundations transforming enterprise and workforce data into reusable, business-ready datasets
Lead discovery across complex or fragmented workforce data domains, including source systems, business definitions, data grain, keys, history, dependencies, quality considerations, and downstream consumption needs
Design scalable data models, transformation layers, and architectures supporting 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
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 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, simplifying fragmented data ecosystems and building 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 practices
Promote a data product mindset centered on business value, reliability, reusability, maintainability, automation, quality, and ownership

Requirements

What you’ll need

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 SQL-based transformation frameworks, dbt or comparable technologies, Apache Airflow or comparable orchestration platforms, and Python
Understanding of 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 other analytical tools
Understanding of trusted, reusable data products for 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; Google BigQuery 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 navigate fragmented data environments, investigate unfamiliar data domains, and lead scalable solutions amid evolving requirements
Experience applying 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
Ability to balance immediate delivery priorities with long-term architecture, reusability, governance, and maintainability
Strong communication skills for explaining 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 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

Benefits

Comp & perks

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
Global hybrid, onsite, or fully virtual working arrangements depending on role and business needs