Principal Analytics Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, and sustain SQL-based data transformations that convert EHR and enterprise-system data into clean, tested datasets ready for analysis.

• Spearhead the creation of reusable enterprise data models and semantic layers focused on patient demand, conversion, engagement, retention, clinician capacity, productivity, scheduling, access, and key performance indicators.

• Set the groundwork for analytics engineering adoption, which includes modeling patterns, development standards, testing protocols, documentation, code reviews, and pathways that lead from analysis to governed production assets.

• Create and manage datasets for enterprise dashboards and analytical products using Power BI or similar tools.

• Collaborate with business leaders, data analysts, product teams, and subject matter experts to transform vague questions into measurable definitions, scalable data products, and actionable insights.

• Provide guidance to teams on model design, metric governance, source-of-truth decisions, data quality, performance, and responsibilities across data engineering, analytics engineering, and BI/reporting.

• Mentor data analysts in SQL, dimensional modeling, version-controlled development, testing, documentation, and the reuse of governed data assets.

• Lead troubleshooting efforts and root-cause analysis for intricate data quality and metric consistency issues, implementing robust controls for trust, lineage, and maintainability.

• Work in tandem with data engineering, platform, governance, and analytics teams on enterprise architecture, as well as privacy and security requirements, EHR transitions, and modernization initiatives.

⛳️ Requirements

• A Bachelor’s degree in Data Analytics, Statistics, Computer Science, Engineering, Informatics, or a related field with relevant work experience is required.

• Over 10 years of experience utilizing advanced SQL to create production-grade data transformations, analytical datasets, and reusable models that support BI, analytics, or data products.

• More than 8 years of experience in designing flexible and scalable analytics and data modeling solutions within healthcare settings, including dimensional models, semantic layers, and dashboards for both executive and operational purposes.

• At least 6 years of experience working independently in a senior or lead capacity, collaborating with business and technical stakeholders to define metrics, clarify ambiguities, and deliver enterprise-scale analytics solutions.

• Proven experience leading analytics engineering architecture and standards across complex environments with multiple data sources, ensuring technical quality and long-term maintainability.

• Expert-level proficiency in analytics data model design and execution using dbt, including knowledge of facts and dimensions, modularity, normalization versus denormalization, incremental processing, testing, lineage, and performance optimization.

• A demonstrated ability to establish foundations for adoption and influence working practices through standards, reusable frameworks, enablement, code review, documentation, and mentoring, without relying on direct authority.

• Strong background in healthcare or a similarly complex regulated industry, with a solid understanding of data privacy, security, minimum necessary access, de-identification, and governance requirements.

• Candidates must be legally authorized to work in the United States.

• Extensive experience with cloud data platforms such as Snowflake, Redshift, or Databricks, and modern transformation and software development practices utilizing dbt, Git, CI/CD, automated testing, and orchestration tools.

• Familiarity with integrating EHR, scheduling, patient access, CRM, workforce, finance, or revenue-cycle data to support reporting on enterprise KPIs, 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 migrating from report-specific logic to reusable governed models.

• An advanced degree in Data Science, Analytics, Computer Science, Informatics, Engineering, or a related field, along with experience mentoring analysts or analytics engineers through formal or informal technical leadership (preferred).

🏝️ Benefits

• Medical insurance

• Dental insurance

• Vision insurance

• AD&D insurance

• Short- and long-term disability insurance

• Life insurance

• 401k retirement savings plan with employer match

• Paid parental leave

• Paid time off

• Holiday pay

• Employee Assistance Program

• Competitive bonus plan