Top Remote Data & Analytics Jobs in San Luis Obispo, CA

Lead data analysis for product and customer decision-making by defining metrics, measuring adoption, engagement, retention, and customer health, and translating complex data into actionable insights. Partner with product, engineering, customer success, and sales teams; automate and improve data processes; support product strategy and customer reviews; strengthen data governance; and mentor analysts.

Lead and develop Analytics Engineers responsible for shared data foundations, platform capabilities, semantic models, developer tooling, and AI-enabled workflows. Define the team vision, operating model, roadmap, standards, and success measures. Partner cross-functionally to prioritize initiatives, improve adoption, guide architecture, and deliver scalable data assets and automation. Drive responsible AI-assisted development, data reliability, testing, documentation, observability, and cost management while contributing hands-on when needed.

Leads enterprise data engineering and platform execution, including modernization roadmaps, cloud transformation, governance, reliability, operational excellence, budgets, vendors, and investment prioritization. Directs multiple engineering managers and teams across platform engineering, data engineering, integration, and operations. Partners with architecture, security, compliance, analytics, clinical, product, finance, and business leaders to deliver secure, scalable, resilient data capabilities supporting AI, interoperability, reporting, and digital transformation.

Transforms complex retail and consumer data into business-ready datasets, scalable pipelines, reporting solutions, automation, and client deliverables. Designs data integration solutions in Unify, troubleshoots connector issues, explores AI-driven improvements, and partners with Product, Engineering, Client Development, Operations, and business stakeholders to define requirements and deliver scalable solutions.

Own client-facing analytics strategy and implementation across GA4, Adobe Analytics, tagging, measurement frameworks, dashboards, experimentation, and GEO measurement. Audit and QA analytics implementations, define KPIs, evaluate incrementality and causal relationships, present executive-ready insights, and collaborate with SEO, paid media, creative, data, engineering, and client teams. Build AI-assisted workflows, automations, scalable tools, and dashboards while mentoring teammates and managing multiple client workstreams.

Analyze large healthcare datasets and develop complex SQL queries using Azure Synapse/Databricks. Build Power BI reports, produce monthly reconciliations and ad-hoc analyses in Excel/PowerPoint, support payments to value-based specialists, evaluate and expand value-based programs, automate processes with Power Automate/Power Apps, and communicate findings to senior leadership while spearheading new technologies.

Lead product analytics: build and maintain dbt models and Looker dashboards, define KPIs, analyze funnel and user journeys, design and evaluate A/B tests, size roadmap impact, and deliver partner and ad-hoc insights to inform product and executive decisions.

Analyzes complex claims data to create SQL- and Power BI-based dashboards, reports, scorecards, forecasts, and actionable insights. Identifies claims leakage, payment anomalies, fraud indicators, reserve activity, and operational inefficiencies. Partners with Claims Operations, IT, Data Engineering, and leadership to improve reporting reliability, business performance, and decision-making. The role also supports statistical analysis, reporting governance, and AI-assisted analytics initiatives.

Work across data infrastructure and analytics: build and maintain dbt models, ingest and transform data, create dashboards and semantic layers in Power BI, validate AI-generated code, partner with stakeholders to deliver analyses and data products, and improve data governance and documentation.

Lead development and scaling of financial analytics: build data models, dashboards, and reporting; translate financial data into executive insights; embed predictive analytics; partner with Finance, IT, and business units to improve data quality, governance, forecasting, and automation.