Manager, Semantic Layer Engineering
Description
Manager, Semantic Layer Engineering
Who are we looking for?
Choice Hotels, one of the world’s largest lodging franchisors, is looking for a Manager, Semantic Layer Engineering to build and maintain a semantic layer, a single source of truth for business metrics across reporting, operations, and AI . The Advanced Analytics team at Choice is the team that makes sure everyone across the company — from the franchise owners running our hotel s to our com mercial teams and senior leaders — is working from the same trusted numbers, whether those numbers show up in a report, an operational system, or an AI assistant . As a key member of our Advanced Analytics team , you will build the governed, machine-readable metric definitions that consolidate logic across dashboards and spreadsheets into one certified foundation every report, system, and AI consumer can trust .
Are you a hands-on data leader who thinks in metric definitions, holds a high bar for governance and documentation, and is energized by turning inconsistent business logic into trusted, code-based standards ? We invite you to apply today for our Manager, Semantic Layer Engineering role and #MakeItYourChoice .
Your Responsibilities
Semantic Layer Architecture & Metric Quality
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Build and maintain the enterprise semantic layer as the default source for governed metrics consumed by dashboards, operational systems, data science, and AI agents, following established semantic layer and metrics tooling standards set by governance leadership.
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Implement metric definitions as version-controlled, tested, reusable logic — including SQL, transforms, and joins against AWS Redshift, S3, and other systems — in alignment with established business meaning and product-defined governance criteria.
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Execute performance, reliability, and cost optimization tasks for metric query execution, following established observability practices and escalating significant infrastructure decisions for supervisor review.
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Operate automated test coverage, SLA monitoring, lineage tracking, anomaly detection rules, and incident response processes for enterprise-certified metrics, following documented quality standards.
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Review proposed changes to enterprise-certified metric definitions and support technical change approvals in coordination with the defining product manager, ensuring all updates follow established change management protocols and propagate through the semantic layer.
Metric Lifecycle & Governance Operations
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Execute the metric certification lifecycle, administering promotion gates through which metrics graduate from exploration to product-scoped use to enterprise certification based on usage, quality, and cross-team impact.
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Work with product managers under a shared ownership model — product defines the metric need, meaning, quality thresholds, and freshness expectations — and execute implementation, monitoring, and enforcement activities.
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Identify metric inconsistencies within assigned functional areas, consolidating duplicate and conflicting metric logic into shared, governed definitions (e.g., a single enterprise definition of RevPAR), and escalating cross-team conflicts that cannot be resolved at the team level to the Senior Director for governance review.
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Document workflows, build processes, and approval paths for metric proposal , validation, certification, and consumption .
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Maintain accurate records of metric lifecycle status, quality results, and open issues, providing regular and ad hoc status updates to the Senior Director to support governance oversight.
AI Enablement & Trusted Consumption
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Expose governed metrics and business context to BI platforms (Tableau), operational systems, data science, and AI agents, following established integration patterns so that new products, reports, and AI consumers inherit trusted definitions automatically .
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Maintain governed business context within the supporting data catalog so that generative AI applications operate on certified business metadata as well as semantic logic, in alignment with catalog standards set by the Manager, Governance & Stewardship.
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Operate monitoring and alerting workflows for metric quality, freshness, and usage in the analytics platform, in partnership with the Manager, Governance & Stewardship, providing regular reports on consumption patterns and anomalies.
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Support prototyping and iteration on integrated data catalog and data fabric capabilities in partnership with the Manager, Governance & Stewardship, who serves as product owner for the enterprise data catalog.
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Research emerging semantic layer, metadata, and AI-enablement tooling and surface findings and potential adoption options to the Senior Director for evaluation and decision-making.
People Management & Stakeholder Coordination
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Directly manage and develop one direct report, closely monitoring their day-to-day tasks and assignments, providing consistent coaching and support, and recommending development opportunities and staffing decisions to the Senior Director for review and approval.
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Execute assigned phases of the semantic layer and governance roadmap under the direction of the Senior Director, contributing to the organization ’ s progress from identifying key metrics and owners to embedding governance directly in systems.
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Support the expansion of the semantic layer as the trusted default for enterprise reporting and AI — earning adoption by making it the most reliable, easiest-to-use source and reducing reliance on shadow logic and one-off definitions, within established timelines and priorities.
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Communicate progress, metric quality status, and delivery updates to stakeholders as directed, and provide timely reporting to the Senior Director to support broader communications with business unit leadership.
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Maintain documentation and ownership records that make every governed metric traceable, explainable, and audit-ready .
Your Experience, Skills & Competencies
Required
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Bachelor ’ s degree or equivalent practical experience in computer science, analytics engineering, data engineering, or a related field.
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5+ years of progressive experience in analytics engineering, data engineering, or BI platform roles, including building governed metrics, reusable data models, and enterprise-scale reporting infrastructure (hands-on semantic-layer ownership a strong plus).
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Solid, demonstrable SQL skills, with experience in Redshift or other columnar databases; ability to write, optimize , and review complex analytical queries in a production environment.
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Hands-on experience implementing semantic layer or metrics-store tooling (e.g., dbt Semantic Layer/ MetricFlow , Cube, AtScale , LookML , or similar), including version control (git), CI/CD, and automated testing practices.
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Working knowledge of data modeling concepts — dimensional modeling, joins, aggregation logic — performance optimization, and semantic layer design principles.
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Demonstrated experience executing data quality frameworks in production: automated tests, SLAs, monitoring, alerting, and incident response for trusted data products.
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Experience with Tableau or comparable BI platforms, including how governed, certified data sources power enterprise reporting at scale.
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Strong attention to detail with accountability for the accuracy, consistency, and governance of enterprise metric definitions consumed by leadership, franchisees, and AI systems.
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Strong communication skills, with the ability to coordinate on metric definitions and methodologies across product , analytics, and business teams, and to provide clear and regular status updates to management.
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Demonstrated ability to manage multiple concurrent tasks and priorities while maintaining data quality and delivery timelines.
Preferred
- Advanced degree in a quantitative