Data Lead - Remote Patient Monitoring

Role

We're hiring a Data Analytics Lead - Remote Patient Monitoring (RPM) to define the KPIs, data models, and analytical infrastructure that support Cadence's clinical, operational, and product decisions. You will partner closely with care delivery, operations, product management, and customer success to build a scalable analytics roadmap and translate complex data - including patient vitals, EHR records, and clinical outcomes - into insights that drive strategy. This role sits at the center of how Cadence measures and improves care delivery at scale.

What You'll Do

• Build and maintain labor productivity models and workforce supply/demand forecasts that give clinical operations leadership a clear, real-time picture of staffing needs, capacity constraints, and efficiency trends across the RPM program.
• Develop financial and operational models, including revenue forecasting and cost-per-patient analyses, that support resource planning and strategic decision-making at the executive level.
• Explore patient vitals, EHR data, and clinician-generated data to surface insights that benefit clinical care, inform product strategy, and support care gap closure across Cadence's patient population.
• Build reusable analytical workflows and automation that reduce manual overhead, accelerate insight generation, and raise the quality and consistency of outputs across the analytics function.
• Collaborate with internal stakeholders to identify data needs, define what data should be collected, and ensure metrics are consistently defined and reliable across teams.
• Maintain and evolve Cadence's data stack (Snowflake, Fivetran, dbt) with well-documented, scalable infrastructure — and collaborate across teams to define how data is collected, structured, and standardized as a reliable foundation for analytical work.

What You Need

• 5+ years of experience working with SQL or other data querying languages, with hands-on experience building and maintaining analytics, reporting, and dashboarding solutions.
• Proficiency with modern data stack tools such as Snowflake, Fivetran, and dbt, or equivalent technologies.
• Bachelor's degree in Mathematics, Statistics, Economics, Computer Science, or a related quantitative field, or equivalent practical experience.
• Practical knowledge of statistics and data analysis techniques, with the ability to translate findings clearly for both technical and non-technical stakeholders.
• Experience working with clinical or health data - including patient vitals, EHR records, or outcomes data - and familiarity with the data structures and sensitivity considerations that apply.