Top Healthtech Data & Analytics Jobs

Provides enterprise decision support through quantitative analysis, statistical modeling, data integration, and business recommendations. Develops and maintains data models, transforms disparate data sources, applies data-hygiene methods, documents methodologies, performs ad hoc analysis, and identifies operational improvements. Collaborates cross-functionally to influence solution strategies and implement measurable business changes. Uses Excel, Power BI, Snowflake, and DAX Studio while managing multiple priorities and communicating complex findings to stakeholders.

Processes client-requested data changes, creates data transfer packages, and writes SQL queries against Microsoft SQL databases. Conducts risk assessments, verifies data updates and transfer datasets, creates stored procedures, monitors data across databases, and supports clinical study implementation. Requires strong attention to detail, documentation, communication, prioritization, and occasional after-hours technical support.

Design, build, operate, and optimize large-scale data pipelines and foundational data platforms for healthcare datasets. The role focuses on Python, SQL, Spark, Airflow, AWS, storage optimization, schema evolution, data quality, observability, troubleshooting, CI/CD, and production support. The engineer will develop trusted data products, improve platform reliability and efficiency, contribute to architecture and technical direction, collaborate cross-functionally, and mentor teammates while using AI-assisted engineering tools.

Designs, develops, and supports enterprise analytics solutions using Power BI, Microsoft Fabric, SQL, semantic models, dashboards, reports, dataflows, and automation. Partners with operational, clinical, financial, and technology stakeholders to translate business needs into trusted insights. Advances governed self-service analytics, data quality, security, documentation, AI-enabled analytics, visualization, and reporting automation while supporting continuous improvement and an on-call rotation.

Own and build reliable analytics data products: define metrics, create and maintain dbt models, design dashboards for growth/finance/ops, improve data quality via testing and monitoring, and enable self-serve analytics for stakeholders.

Build, maintain, and optimize cloud data pipelines using SQL, Python, Snowflake, BigQuery, and dbt. Integrate data from APIs, databases, cloud storage, marketing platforms, and third-party sources. Implement data quality checks, monitoring, troubleshooting, automation, and performance improvements. Collaborate with architects, analytics teams, and stakeholders to deliver reliable, reusable, analytics-ready datasets.

The Senior Data Analyst translates commercial business needs into technical data specifications, writes complex SQL queries, extracts and validates large datasets, and automates workflows with Python. The role delivers accurate analytics and reporting, communicates findings to technical and non-technical stakeholders, supports pre-sales and RFP activities, manages competing requests, and collaborates with Operations, Strategy, and Data Engineering teams.

Develop and enhance trade and commercial reporting solutions using Snowflake, Tableau, and related BI tools. Modernize manual and legacy reporting workflows into automated dashboards and scalable analytical solutions. Translate queries and business rules across data platforms, validate reporting accuracy, investigate discrepancies, document workflows, and partner with commercial and senior stakeholders to deliver actionable insights.

Analyzes commercial, operational, market, and customer data to identify trends, risks, and opportunities. Develops dashboards, reporting templates, visualizations, and actionable insights for sales, marketing, finance, contracting, and leadership teams. Supports forecasting, segmentation, promotional evaluation, data governance, process improvements, UAT, system enhancements, strategic initiatives, and ad hoc modeling. Communicates findings to technical and non-technical stakeholders and documents analytical methodologies.

Designs and delivers backend systems, APIs, data models, ETL workflows, data interactions, and user interfaces. Optimizes data operations for scalability, performance, integrity, and security while contributing to automated testing and CI/CD. Requires strong Python and Apache Spark experience, distributed computing knowledge, Agile experience, and data analysis skills. The role supports healthcare-focused digital services in a remote-first environment.