Top Healthtech Data & Analytics Jobs

Leads the design, development, and maintenance of scalable data pipelines, ETL/ELT processes, enterprise data warehouses, and data integrations. Establishes data models, quality standards, governance practices, and documentation while ensuring healthcare data security and interoperability through HL7, FHIR, APIs, and SFTP. Partners with clinical, operational, analytical, and IT stakeholders to deliver reliable data solutions, optimize performance, resolve data issues, and mentor junior staff.

Analyze large-scale electronic health record and real-world data using SQL, R, and Python. Scope requests, produce research studies and results, write Databricks SQL regex queries, support customers, investigate data sources, collaborate on peer-reviewed publications, and create enablement materials for researchers.

Analyze customer retention and lifecycle data across CRM, loyalty, e-commerce, email, SMS, and subscription channels. Build churn prediction models, cohort and LTV analyses, customer journey insights, loyalty program reporting, SQL transformations in Snowflake, data preparation workflows, and Tableau dashboards. Partner with Brand and Retention teams to improve campaign performance, customer loyalty, repeat purchases, and revenue growth.

Engineers, implements, optimizes, automates, and supports enterprise database and cloud data platforms. Designs secure, highly available solutions across SQL Server, Azure, PostgreSQL, Oracle, Snowflake, and analytical technologies; provides Level 3 production support; leads modernization, cloud migration, performance optimization, disaster recovery, security, governance, and automation initiatives. Partners with architects and operations teams, develops standards and runbooks, troubleshoots critical incidents, supports CI/CD and data integration, and mentors technical staff.

Operationalize and manage enterprise governance across AI, automation, data and analytics, and product investments. Own governance policies, exceptions, committee operations, maturity tracking, stakeholder requirements, risk scoring, data catalog and lineage processes, security and privacy controls, reporting, and user enablement. The role requires business analysis, data governance, AI risk assessment, regulatory awareness, Agile delivery, and collaboration with technical and non-technical stakeholders.

Build and operate production data pipelines that ingest, validate, transform, and govern clinical and operational data. Deliver governed metrics, self-service analytics, and observability across teams. Partner with engineering, product, clinical, and GTM to ensure accurate, reproducible, and defensible metrics; automate ingestion, validation, and transformation using modern tooling and cloud data warehousing.

Build and optimize end-to-end data pipelines, production-grade dbt models, BigQuery schemas, Fivetran integrations, and Looker semantic layers. Partner with clinical, operations, finance, and growth teams to translate business questions into analytics projects focused on acquisition, attribution, conversion, and funnel performance. Establish data quality, governance, documentation, testing, and version-control practices while mentoring analysts and supporting secure, compliant data use.

This internship provides hands-on experience in data science, analytics, and governance. Responsibilities include analyzing health data, creating visualizations and business insights, supporting data quality and metadata management, applying analytics technologies, and collaborating with business teams. Interns will learn Agile practices and explore topics including cloud technology, AI/ML, ethical AI, and responsible data use.

Lead the technical strategy and architecture for a scalable clinical data platform. Drive cross-team engineering initiatives, redesign legacy data pipelines, build batch and streaming systems, develop deployment and self-service tooling, improve monitoring, manage data models and governance, ensure privacy and security, and mentor engineers. Partner with product, AI, infrastructure, health systems integration, and business intelligence teams to deliver reliable, extensible data capabilities.

Supports payer-related platform operations through SQL-based data analysis, reporting, validation, reconciliation, issue tracking, and documentation. Partners with cross-functional stakeholders to investigate operational issues, communicate updates, monitor workflows, and improve processes. Uses Excel, Jira, cloud data platforms, and AI tools to manage requests, summarize findings, support testing, and maintain accurate operational records.