Top Remote Analysis Reporting Jobs in San Diego, CA

Lead end-to-end product analytics: instrumentation, data quality, funnels, retention, and experimentation. Use SQL and Python for analysis, build dashboards, partner with product and growth, and improve analytics infrastructure (Amplitude and data warehouse migration). Remote with GMT+1 overlap.

Lead analytics to improve player experience, engagement, retention, and monetization. Analyze player lifecycle, run cohort and funnel analyses, design and evaluate A/B tests, build dashboards, define product metrics, investigate KPI changes, and partner with product, marketing, engineering, and data teams to deliver data-driven recommendations and improve data quality.

Design, develop, and deploy Power BI reports and dashboards. Gather requirements, model and transform data, write DAX and SQL, integrate multiple data sources, ensure data security, and support end users.

Work with clients and technology teams to gather and translate business requirements into technical user stories and solutions, propose process improvements, and communicate effectively with stakeholders. Assist in analytical solution delivery and may write/execute SQL queries.

Lead design and scaling of backend data infrastructure for Beacon's Datastore: build event-driven pipelines, data models, and APIs; draft RFCs; integrate services; improve operational robustness, documentation, and tooling to support clinical and scientific workflows.

Manage CVS Health's data partnerships and monetization strategy, source new partnerships, ensure operational success, and drive data product adoption across analytics teams.

The VP of Data will oversee 9amHealth's data functions including data engineering, analytics, and data science while collaborating with executive teams to shape data-driven strategies and improve member experiences in chronic condition management.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

The Data Engineer will build and maintain data platforms, deploy pipelines, monitor transformations, and collaborate with Data Scientists and DevOps.

Lead the Decision Science function, designing ML models, overseeing experimentation frameworks, and ensuring model adoption across the company, driving operational and commercial improvements.