Principal Analytics Engineer

LifeStance Health

1d•$160,000 - $175,000•Remote

About The Position

LifeStance Health is seeking an experienced Principal Analytics Engineer to provide technical leadership for Enterprise Analytics. This senior individual contributor reports directly to the VP of Analytics and Insights and will create the trusted, scalable analytics foundation used to understand enterprise performance and support better organizational decisions. The role designs high-quality data models and analytics products spanning patient demand and conversion, access and scheduling, patient engagement and retention, operational optimization, enterprise KPIs, and business transformation initiatives. The position remains hands-on with SQL, data modeling, testing, and documentation while establishing analytics engineering standards, accelerating adoption of analytics engineering practices, and mentoring data analysts. It partners with business and technical stakeholders to translate ambiguous questions into metric definitions, scalable data products, and actionable insights. Team members may also participate in projects and perform responsibilities not specifically outlined in the job description in support of the organization’s mission.

Requirements

Bachelor’s degree in Data Analytics, Statistics, Computer Science, Engineering, Informatics, or another discipline with relevant work experience.
At least 10 years of experience using advanced SQL to build production-grade data transformations, analytical datasets, and reusable models supporting BI, analytics, or data products.
At least 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.
At least 6 years of experience operating independently in a senior or lead role and 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 analytics data-model design and execution skills with dbt, including facts and dimensions, modularity, normalization and denormalization trade-offs, incremental processing, testing, lineage, and performance optimization.
Ability to establish 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.
Legally authorized to be employed in the United States.
Awareness, inclusivity, sensitivity, humility, and experience working with individuals from diverse ethnic backgrounds, socioeconomic statuses, sexual orientations, gender identities, and other aspects of culture.

Nice To Haves

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 for 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.

Responsibilities

Architect, build, and maintain SQL-based transformations that convert EHR and other enterprise-system data into clean, tested, analysis-ready datasets.
Lead the design of reusable enterprise data models and semantic layers for patient demand and conversion, engagement and retention, clinician capacity and productivity, scheduling and access, and other priority KPIs.
Establish analytics engineering modeling patterns, development standards, testing practices, documentation expectations, code-review approaches, and pathways from analysis to governed production assets.
Develop and maintain datasets for enterprise dashboards and analytical products in Power BI or similar tools, supporting actionable insights and self-service capabilities.
Partner with business leaders, data analysts, product teams, and subject matter experts to define metrics and deliver scalable data products and decision-ready insights.
Advise teams on model design, metric governance, source-of-truth decisions, data quality, performance, and the 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.
Lead troubleshooting and root-cause analysis for complex data-quality and metric-consistency issues, and implement durable controls that improve trust, lineage, and maintainability.
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 efforts.