Senior Analytics Engineer, Health Operations (MSO)

Salary

$175,500 - $263,800

Location

Cupertino, CA

Posted

Today

We are seeking a senior, full-stack Analytics Engineer who is passionate about crafting quantitative models and data solutions that have a direct, measurable impact on the health and wellness experience of Apple customers. In this role, you will own the analytical frameworks, measurement architecture, and diagnostic models that tell the story of how our health operations are performing and where it is headed. A core responsibility of this role is data analytics engineering—designing robust data models, transforming complex health operations datasets, and establishing scalable data structures that power reliable reporting and advanced analytics. You bring the rare ability to build rigorous, scalable data solutions and translate what they reveal into forward-looking insights that influence how clinical and operational leaders plan, invest, and act. If you believe that the most powerful data science isn't just about answering questions, but also shaping the questions worth asking, this role was built for you.

You will own the design, deployment, and evaluation of end-to-end measurement plans from the ground up for the launch of new health services and features. You will partner closely with cross-functional partners across clinical, operational, product, legal, and engineering teams to define how the service measures success, tracking adoption, operational efficiency, and customer experience to translate complex data into insights that drive real decisions. Beyond the immediate launch scope, you will serve as a technical and strategic leader within the team, driving analytics engineering best practices, and helping shape what a world-class healthcare analytics function looks like at scale. Operating with high autonomy, this is a high-impact role for someone who pairs technical rigor and data visualization, with exceptional executive presence and a passion for mission-driven impact.

Design, develop, and maintain clean, production-grade data models and transformation pipelines that translate complex health operations and clinical data into reliable, scalable analytical datasets.
Own the design, development, and ongoing governance of analytical frameworks and KPI systems for health operations, defining what the business measures, establishing baseline metrics, and building the measurement infrastructure that makes those data actionable across operational and clinical teams.
Conduct deep descriptive, diagnostic, and exploratory analyses on complex healthcare datasets to uncover underlying drivers, surface key operational trends, and translate quantitative findings into strategic, data-driven recommendations.
Define, deploy, and evaluate comprehensive measurement plans for new health service and product launches, tracking adoption, operational efficiency, financial performance, and patient experience to guide iterative improvements and executive decision-making.
Serve as the primary analytical partner to health operations leadership partnering to define goals, pressure-test assumptions, evaluate business impact, monitor performance against plan, and proactively surface risks and opportunities.
Build polished, scalable data visualizations and executive-level reporting products (via Tableau, AWS QuickSight, or equivalent) that distill analytical concepts into clear, compelling, and actionable business narratives.
Champion analytics standards and best practices across the team, and research / adopt modern data science methods, tools, and workflows across the team to elevate technical capabilities, consistency, and scale.
Partner closely with data engineering teams to design scalable, data models and reliable pipelines, ensuring analytics solutions are structured for self-serve access as an enterprise single source of truth.
Bachelor's Degree in Statistics, Biostatistics, Business, Healthcare, Operations Research, Computer Science, or a related field.
7+ years of experience in business insights, or strategic analytics with at least 2 years in a lead or senior individual contributor capacity.
Proven track record of applying quantitative methods, statistical modeling, and business /financial analysis to evaluate product performance, operational workflows, and executive priorities.
Proven ability to design end-to-end measurement frameworks, including metric definitions, calculation logic, and validation methods, from zero to one for new initiatives.
Demonstrated ability to influence clinical and operational stakeholders and drive cross-functional alignment without direct authority.
Proven ability to translate complex, ambiguous business problems into structured analytical frameworks and actionable recommendations.
Advanced SQL proficiency and experience with Python or equivalent for data wrangling, exploratory data analysis, and building automated, production-ready analytical workflows.
Demonstrated track record of building intuitive data visualizations, executive dashboards, and reports using tools such as Tableau or AWS QuickSight.
Exceptional executive presence and storytelling abilities, with demonstrated success translating technical findings into clear narratives for clinical, operational, and non-technical stakeholders.
Highly organized, self-directed, and proven ability to navigate ambiguity, prioritize competing demands, and deliver high-impact results under tight timelines.
Ability to be onsite; this role is a hybrid, in-person position
Deep business acumen with the ability to rapidly understand health operational workflows, clinical program performance, and patient experience drivers and apply that context to build strategically meaningful analyses.
Familiarity with formal performance management and goal-setting frameworks (e.g., OKRs, operational scorecards, quality benchmarks) and how data infrastructure supports accountability at scale.
Background in health operations, clinical operations, or healthcare delivery analytics with an understanding of how patient experience, clinical quality, care utilization, and operational efficiency metrics work together to shape care outcomes.
Hands-on experience with modern data orchestration tools and an active interest in applying machine learning, LLMs, or AI assisted workflows to accelerate clinical and operational analytics.

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