This role combines strong business acumen with deep technical expertise. The successful candidate will work directly with business and technology stakeholders to understand the decisions and outcomes they are trying to enable, discover and assess underlying data, design appropriate data and transformation architecture, and build reusable analytics data products that can support multiple downstream use cases.
The role will help move WFS from fragmented, report-specific data preparation toward a governed analytics ecosystem where critical data and business logic are reusable, trusted, documented, and engineered for scale.
How will you make an impact in this role?
The Sr. Manager, Analytics Engineering and Data Strategy will lead the development of scalable analytics data foundations and reusable data products that connect enterprise data with workforce metrics, analytics, reporting, AI, and business decision-making.
The role will take complex and sometimes ambiguous business and data problems from discovery through architecture, engineering, adoption, and ongoing ownership. This individual will work across Business, Data Engineering, Architecture, Data Governance, Analytics, and Technology teams to determine how data should be sourced, modeled, transformed, governed, and made reusable across multiple workforce use cases.
The role will also help establish modern analytics engineering practices, architecture principles, and development standards that improve reliability, quality, maintainability, and speed while enabling analysts and business teams to spend less time sourcing, joining, cleaning, and reconciling data and more time generating insights and driving business outcomes.
Build the Analytics Data Foundation
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
Develop Trusted, Reusable Data Products
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
Establish Modern Analytics Engineering Practices
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
Drive Cross-Functional Collaboration and Data Strategy
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
Education & Knowledge
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
Work Experience
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
