ABOUT PEOPLEONE HEALTH:

PeopleOne Health is one of the fastest-growing providers of value-based primary care and has earned the best-in-class member satisfaction scores. We deliver exceptional healthcare that reduces costs and significantly improves health outcomes by focusing on preventive care, behavior change, and keeping people healthier. The key to our successful culture is living our motto: care for yourself; care for each other; care for our members.

JOB SUMMARY:

Position to lead the design, development, and evolution of our healthcare analytics ecosystem. This role will serve as a strategic technical leader responsible for transforming complex healthcare data into trusted insights, scalable data products, operational decision systems, and advanced analytical capabilities that support our mission of improving access, quality, outcomes, and affordability.

The ideal candidate combines strong analytics engineering skills with healthcare domain expertise and advanced analytical thinking. They will build and maintain scalable data models, semantic layers, governance frameworks, reporting solutions, and predictive analytics capabilities while partnering closely with clinical, operational, product, finance, and executive leaders.

This role is ideal for someone who enjoys solving business problems through data, building modern analytics architecture, and helping organizations mature from descriptive reporting to proactive, data-driven decision making.

Within the first year, this individual will strengthen the company’s analytics foundation by improving data quality, expanding semantic modeling capabilities, establishing trusted enterprise metrics, enhancing governance practices, advancing predictive analytics capabilities, and delivering scalable data products that support operational decision-making, client intelligence, and future AI initiatives.

SUPERVISORY RESPONSIBILITIES:

This position provides supervision to others: No

ESSENTIAL JOB FUNCTIONS:

Analytics Engineering & Data Platform Development

• Design, develop, and maintain scalable data models, transformations, and analytics pipelines that support enterprise reporting and operational decision-making.
• Build and optimize analytics-ready datasets from multiple healthcare data sources including electronic medical records, eligibility files, laboratory data, pharmacy data, referral systems, care management platforms, and medical and pharmacy claims.
• Develop reusable data products that support reporting, client performance measurement, operational workflows, and advanced analytics.
• Implement scalable data transformation frameworks and best practices for testing, monitoring, performance optimization, and deployment.
• Partner with engineering teams to support modern cloud analytics platforms including Snowflake, Microsoft Fabric, Databricks, or similar technologies.

Semantic Modeling & Enterprise Metrics

• Design and maintain enterprise semantic models that provide consistent definitions and calculations across reporting, analytics, and decision support solutions.
• Define, document, and govern key healthcare, operational, financial, and client-facing metrics including engagement, utilization, PMPM, access, quality, referral performance, leakage, and ROI.
• Establish trusted analytical frameworks that ensure consistency across departments, clients, products, and reporting environments.
• Partner with business leaders to translate complex operational questions into scalable analytical solutions and governed business metrics.
• Support the evolution of self-service analytics through reusable datasets, semantic models, and well-defined data products.

Reporting, Visualization & Decision Support

• Design and deliver dashboards, scorecards, executive reporting, and operational decision systems that drive measurable business outcomes.
• Build solutions that provide actionable insights to clinical, operational, finance, product, and executive stakeholders.
• Develop client-facing reporting and performance measurement solutions that clearly communicate value, outcomes, savings, and operational performance.
• Support automated monitoring, alerting, and performance tracking capabilities that improve operational efficiency and decision-making.

Advanced Analytics & Predictive Insights

• Apply statistical analysis and advanced analytical techniques to identify trends, opportunities, risks, and performance drivers across clinical, operational, financial, and engagement data.
• Develop forecasting models, predictive analyses, segmentation strategies, and risk identification approaches that support business and operational decision-making.
• Partner with stakeholders to identify opportunities where advanced analytics can improve member engagement, utilization management, care navigation, operational effectiveness, and client outcomes.
• Design analytical assets and datasets that support future machine learning, AI-enabled workflows, and intelligent decision support capabilities.
• Translate analytical findings into practical recommendations and business actions that can be understood and applied by non-technical audiences.

Data Governance, Quality & Trust

• Establish and maintain governance practices including business glossaries, metric ownership, data stewardship, metadata management, documentation standards, and data lineage.
• Implement automated data quality monitoring, reconciliation processes, anomaly detection, and validation frameworks for critical datasets and metrics.
• Ensure transparency, consistency, and auditability of data transformations, calculations, and reporting methodologies.
• Partner with stakeholders to drive adoption of trusted enterprise metrics and standardized reporting practices.
• Support compliance with healthcare privacy, security, and regulatory requirements including HIPAA.

Strategic Partnership & Innovation

• Collaborate with clinical, operational, product, finance, and executive leaders to define measurement strategies and analytical roadmaps.
• Translate ambiguous business challenges into scalable analytical solutions and measurable outcomes.
• Support strategic initiatives involving population health, value-based care, employer-sponsored healthcare, care navigation, member engagement, and healthcare affordability.
• Contribute to the organization’s long-term analytics, governance, and AI strategy.
• Act as a trusted advisor on healthcare analytics, measurement methodologies, and data-driven decision making.

SKILLS & ABILITIES:

• Advanced SQL skills with demonstrated experience working with large, complex datasets.
• Strong communication skills with the ability to work effectively with technical and non-technical stakeholders.
• Demonstrated ability to translate business questions into actionable analytical solutions.

EDUCATION & CERTIFICATIONS :

• Bachelor’s degree in Computer Science, Information Systems, Data Science, Statistics, Mathematics, Engineering, or equivalent experience.

EXPERIENCE (Required)

• 7+ years of experience in analytics engineering, healthcare analytics, business intelligence, data science, or related disciplines.
• Strong Python experience for analytics, automation, and data processing.
• Experience designing dimensional, semantic, and analytical data models.
• Experience building production reporting solutions, dashboards, scorecards, and operational analytics applications.
• Experience developing advanced analytical solutions including forecasting, predictive analysis, segmentation, or statistical analysis.
• Experience defining and governing business metrics and analytical methodologies.
• Experience working with cloud-based analytics platforms such as Snowflake, Microsoft Fabric, Databricks, Redshift, BigQuery, or equivalent technologies.

EXPERIENCE (Preferred)

• Experience within healthcare, population health, value-based care, direct primary care, employer-sponsored healthcare, or healthcare services organizations.
• Experience with semantic layer technologies such as Power BI Semantic Models, dbt Semantic Layer, LookML, or similar platforms.
• Experience implementing data governance, metadata management, lineage, and data quality frameworks.
• Experience supporting machine learning, AI, or advanced analytics initiatives.
• Experience with healthcare interoperability standards including HL7, FHIR, and healthcare data integration practices.
• Experience building operational decision systems, analytical data products, or intelligent workflow solutions.
• Familiarity with data cataloging, observability, and governance technologies.

PHYSICAL REQUIREMENTS:

(The physical requirements described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the position’s essential functions.)

• Office or other indoor work with minimal physical demands such as occasionally lifting/moving materials less than 25 pounds.