Top Healthtech Data & Analytics Jobs

Owns governance, quality, integrity, and operational management of contract and customer master data. Oversees contract configuration, pricing controls, member onboarding, eligibility, roster management, exception resolution, metrics, and process documentation. Leads continuous improvement using Lean and Six Sigma methodologies, develops standardized controls, and manages a data operations team while collaborating with Sales, Finance, Reconciliation, IT, and other functions.

Analyzes healthcare data using SAS, SQL, Excel, R, Python, Tableau, and related tools to identify trends, variances, outliers, and improvement opportunities. Creates reports, dashboards, tables, and visual presentations for technical and non-technical stakeholders. Supports healthcare intervention measurement, data entry, database functions, documentation, research, and policy compliance.

Own and maintain Dermalogica's global reporting and analytics ecosystem. Build, validate, and troubleshoot Power BI/Tableau dashboards and data flows, ensure data quality and governance across sell-in/sell-out datasets, provide ad hoc analysis and monthly performance tracking, and develop analytical models to inform global strategy and identify growth opportunities.

Leads Covista’s enterprise data engineering strategy and operations, including the cloud data lakehouse, ingestion pipelines, governance catalog, DataOps, data contracts, lineage, and PII controls. Manages platform reliability, cost optimization, technical debt, resource capacity, vendors, and proof-of-concept initiatives. Partners with data science, reporting, AI, and business stakeholders to ensure trustworthy data. Builds and mentors a diverse technical team while driving platform modernization, operational excellence, and scalable delivery.

Analyze strategic business problems using SQL, Python, statistics, data science, and AI tools. Develop KPIs, self-service analytics, automated monitoring, dashboards, and AI-powered workflows. Partner with Product, Operations, Marketing, Finance, and Clinical Operations to identify opportunities, generate actionable insights, improve forecasting, and support strategic decisions. Communicate recommendations to leadership and build scalable, data-driven operational processes.

Supervise analysts and reporting coordinators delivering accurate analytics, dashboards, and data outputs. Review and validate reports, lead complex data modeling and KPI initiatives, translate business questions into analytical solutions, improve query and reporting processes, and modernize the reporting stack through automation and self-service capabilities. Provide technical mentorship, establish data governance and documentation standards, manage performance, and support continuous improvement across the analytics function.

Designs, builds, and operates scalable data platforms and ELT pipelines across Google Cloud using dbt, Python, Airflow, and BigQuery. Responsibilities include data modeling, governance, security, cost optimization, observability, infrastructure as code, incident response, and end-to-end feature delivery. The role pilots AI-assisted engineering practices, develops reusable tooling, collaborates with business and technical stakeholders, and mentors data engineers.

Leads marketing measurement and analytics strategy, including Marketing Mix Modeling, Multi-Touch Attribution, experimentation, dashboards, forecasting, and budget optimization. Manages 8–12 data scientists and analysts, partners with marketing and finance teams, and presents recommendations to senior leadership to improve campaign performance and financial outcomes.

Design and maintain scalable ETL/ELT pipelines, analytical dashboards, and reporting tools supporting clinical, research, and operational priorities. Write production-quality Python, R, and SQL; develop data architecture; partner with data scientists to operationalize analytics; integrate AI-enabled workflow improvements; establish engineering standards; and mentor junior team members.

The Data Business Analyst will document enterprise data concepts, support conceptual and logical data modeling, and implement data governance standards. Responsibilities include maintaining data ownership, glossaries, quality rules, lineage, metadata, and data catalogs; monitoring data quality; resolving inconsistencies; and partnering with business and technology stakeholders to improve data processes, security compliance, accessibility, and reliability for operational decision-making and AI initiatives.