Senior Analytics Engineer, Clinical Quality and Outcomes

Behavioral Health Practice Services LLC, operating as LifeStance Health, is hiring a Senior Analytics Engineer to support its clinical quality and outcomes analytics capabilities. The role builds governed clinical datasets, reporting and dashboards, and research-grade data assets to inform clinical decision-making, quality improvement, regulatory readiness, and research initiatives.

Responsibilities:

• Design, build, and maintain SQL-based data transformations that convert EHR and clinical source data into clean, reliable, analysis-ready datasets that are actionable and translatable to our frontline clinicians
• Partner with clinical, operational, and executive leaders to translate clinical questions into measurable analytic requirements, data models, and dashboard experiences, using electronic health record data
• Serve as a trusted thought partner for clinical quality, outcomes measurement, patient safety, regulatory readiness, and research-based reporting initiatives
• Identify emerging opportunities to leverage advancements in analytic AI-enabled quality monitoring, and modern healthcare data platforms in the clinical environment
• Work collaboratively with analytics team members across functions to ensure consistency in practice and reporting across functions: Operations, Clinical Operations, Payor Relations, Business Development
• Maintain rigorous data quality controls, validation processes, lineage documentation, and metric governance standards to ensure trust in clinical reporting
• Design repeatable testing and reconciliation frameworks that identify discrepancies before they reach operational, executive, board, or external reporting audiences
• Ensure consistency of metric definitions and calculations across dashboards, reports, scorecards, and enterprise reporting environments
• Apply and uphold data security and privacy best practices for healthcare and health research datasets, including data de-identification standards and minimum-necessary use principles
• Troubleshoot and resolve data quality issues in clinical datasets, performing exploratory analysis and validation to ensure accuracy and consistency of reported metrics
• Document data models, pipeline logic, and dashboard definitions to support transparency, maintainability, and knowledge-sharing across the analytics team
• Collaborate with enterprise data engineering, analytics engineering, and data analysts to align clinical data models with enterprise data architecture and governance standards
• Lead the design of intuitive, user-centered dashboard experiences that enable clinicians, medical leaders, operations leaders, and executives to quickly identify insights and take action
• Translate workflow requirements into dashboards that support both operational intervention, strategic decision-making, and quality improvement
• Design reporting experiences that balance flexibility, self-service analytics, and standardized enterprise measures
• Apply user-centered design principles to ensure dashboards are adopted, trusted, and operationally relevant
• Develop executive, board, regulatory, and clinician-facing reporting solutions that provide a consistent source of truth across audiences
• Build and curate research-grade datasets that support clinical outcomes studies, publication efforts, payer reporting, and real-world evidence generation
• Partner with clinical and research leaders to operationalize outcomes measures, patient-reported outcomes, and quality indicators into analytically rigorous datasets
• Ensure data are appropriately validated, documented, and reproducible for internal and external research use
• Design and maintain scalable analytics models, data marts, and reporting layers using cloud based data warehouses (AWS Redshift, Snowflake) and modern cloud data architecture
• Develop and optimize SQL-based transformations supporting clinical quality, patient safety, outcomes, and research reporting
• Leverage modern analytics engineering practices. For example: dbt, automated testing, version control, documentation, and CI/CD methodologies
• Collaborate with enterprise data engineering teams to ensure clinical analytics solutions align with enterprise architecture and future-state platform strategy

Requirements:

• Bachelor's degree in Data Science, Statistics, Computer Science, Informatics, Engineering, Public Health, or a closely related field
• 7+ years of experience in healthcare analytics, analytics engineering, business intelligence, data science, or related disciplines
• 5 + years experience working directly with clinical subject matter experts and executive stakeholders to define, refine, and operationalize complex measures
• Demonstrated experience designing enterprise reporting solutions that support executive, operational, clinical, and regulatory decision-making
• Advanced SQL expertise with experience building scalable analytic datasets and reporting environments
• 5+ years developing Power BI or equivalent business intelligence solutions
• Strong experience with Snowflake and modern cloud-based data platforms
• Experience implementing data quality testing, metric validation frameworks, and governance approaches that ensure reporting accuracy and consistency
• Experience translating stakeholder requirements into highly usable and impactful dashboard experiences
• Strong understanding of healthcare data structures, electronic health record systems, clinical workflows, and quality metrics
• Qualified candidates must be legally authorized to be employed in the United States
• Demonstrates awareness, inclusivity, sensitivity, humility, and experience in working with individuals from diverse ethnic backgrounds, socioeconomic statuses, sexual orientations, gender identities, and other various aspects of culture
• Experience supporting behavioral health analytics, patient safety programs, measurement-based care, quality improvement, or regulatory reporting initiatives
• Experience developing datasets supporting peer-reviewed publications, clinical research, outcomes research, or real-world evidence generation
• Experience with dbt, Git, automated testing frameworks, semantic modeling, and modern analytics engineering practices
• Experience working with PHQ-9, GAD-7, safety planning, suicidality measures, HEDIS, CMS metrics, accreditation standards, or quality reporting programs
• Experience creating executive-level scorecards and board-facing dashboards
• Master's degree or higher in Public Health, Data Science, Biostatistics, Informatics, Health Services Research, or a related field