Principal Analytics Engineer

LifeStance Health is hiring a Principal Analytics Engineer to provide technical leadership for Enterprise Analytics. The role builds trusted, scalable data models and analytics products, establishes analytics engineering standards, and mentors analysts while supporting enterprise decision-making.

Responsibilities:

• Architect, build, and maintain SQL-based data transformations that convert data from EHR and other enterprise systems into clean, tested, analysis-ready datasets with minimal technical direction
• Lead the design of reusable enterprise data models and semantic layers that support consistent analysis of patient demand and conversion, patient engagement and retention, clinician capacity and productivity, scheduling and access, and other priority KPIs
• Establish the foundation for analytics engineering adoption, including modeling patterns, development standards, testing practices, documentation expectations, code-review approaches, and repeatable pathways from analysis to governed production assets
• Develop and maintain the datasets utilized by enterprise dashboards and analytical products in Power BI or similar tools that provide actionable insights and self-service capabilities for operational, functional, and executive stakeholders
• Partner closely and iteratively with business leaders, data analysts, product teams, and subject matter experts to translate ambiguous questions into clear metric definitions, scalable data products, and decision-ready insights
• Serve as an internal expert on analytics engineering, advising teams on model design, metric governance, source-of-truth decisions, data quality, performance, and the appropriate division of responsibilities across data engineering, analytics engineering, and business intelligence/reporting
• Mentor data analysts in SQL, dimensional modeling, version-controlled development, testing, documentation, and reuse of governed data assets, helping analysts progressively adopt analytics engineering methods in day-to-day work
• Lead troubleshooting and root-cause analysis for complex data quality and metric-consistency issues, and implement durable controls that improve trust, lineage, and maintainability across analytics products
• Collaborate with data engineering, platform, governance, and analytics teams to align enterprise models with target architecture, privacy and security requirements, EHR transition needs, and modernization of the data and analytics environment

Requirements:

• Bachelor's degree in Data Analytics, Statistics, Computer Science, Engineering, Informatics, or another discipline with relevant work experience required
• 10+ years of experience using advanced SQL to build production-grade data transformations, analytical datasets, and reusable models supporting BI, analytics, or data products
• 8+ years of experience designing flexible, scalable analytics and data modeling solutions in healthcare environments, including dimensional models, semantic layers, and executive or operational dashboards
• 6+ years of experience operating independently in a senior or lead role, partnering with business and technical stakeholders to define metrics, resolve ambiguity, and deliver enterprise-scale analytics solutions
• Demonstrated experience leading analytics engineering architecture and standards across complex, multi-data-source environments, with accountability for technical quality and long-term maintainability
• Expert-level skills in analytics data model design and execution with dbt, including facts and dimensions, modularity, normalization and denormalization trade-offs, incremental processing, testing, lineage, and performance optimization
• Proven ability to create adoption foundations and influence ways of working through standards, reusable frameworks, enablement, code review, documentation, and mentoring without relying on direct authority
• Strong healthcare or similarly complex regulated-industry experience, with working knowledge of data privacy, security, minimum-necessary access, de-identification, and governance requirements
• 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
• Deep experience with cloud data platforms such as Snowflake, Redshift, or Databricks and modern transformation and software-development practices using dbt, Git, CI/CD, automated testing, and orchestration tools
• Experience integrating EHR, scheduling, patient access, CRM, workforce, finance, or revenue-cycle data to support enterprise KPI reporting, patient journey analytics, capacity and utilization insights, retention analysis, or operational decision-making
• Experience leading analytics modernization, EHR or source-system transitions, semantic-layer development, data product governance, or migration from report-specific logic to reusable governed models
• Advanced degree in Data Science, Analytics, Computer Science, Informatics, Engineering, or a related field
• experience coaching analysts or analytics engineers through formal or informal technical leadership