Top Senior Analytics Jobs in Providence, RI

Build and maintain reliable data models, pipelines, metrics, dashboards, and self-service data products for Airbnb.org. Own data quality, testing, validation, incident response, and improvements to data assets. Partner with Marketing, Fundraising, Programs, Product, Data Science, and Engineering to translate business needs into trusted data solutions. Lead projects from ambiguous scoping through delivery, communicate technical tradeoffs, and mentor fellow data practitioners.

Lead end-to-end analytics for Netflix Ads demand, including media planning and campaign management tools, machine learning models, and agentic solutions. Partner with scientists, product managers, engineers, and data engineering to develop metrics, reliable analytics data, insights, and recommendations. Independently identify risks and opportunities, shape product roadmaps, and communicate findings to technical and non-technical stakeholders.

Own Hercules’ full data stack, including ETL from third-party providers into BigQuery, data modeling with dbt and medallion architecture, semantic-layer metric definition, and self-service business intelligence in Omni. Build reliable, maintainable systems and dashboards while applying strong software architecture, API design, code quality, and AI-assisted development practices.

Leads data analytics and insights for workspace and amenities, developing reporting tools, predictive analytics, data infrastructure, and trusted data governance practices. Partners with real estate, business, technology, and data teams to translate large datasets into actionable insights, support strategic decisions, and improve analytics capabilities. Presents findings to stakeholders and executives while balancing quality, scalability, speed, and continuous improvement.

Own end-to-end data products, including source onboarding, API and file-feed ingestion, orchestration, incremental and idempotent pipelines, Snowflake and dbt Cloud modeling, testing, documentation, alerting, and production reliability. Establish engineering standards, manage secrets and governance, use AI coding tools responsibly, collaborate with vendors and stakeholders, and mentor junior engineers.

Develop and maintain dashboards, reports, scorecards, KPI solutions, and analytical models that provide business performance insights. Responsibilities include trend and root-cause analysis, forecasting, data validation, stakeholder requirements gathering, reporting automation, process improvement, documentation, and data quality governance. The role partners with cross-functional teams and communicates findings to technical and non-technical audiences.

Own marketing channel performance tracking, reporting, experimentation, dashboards, forecasting, and LTV analysis. Develop measurement frameworks, identify data issues, and translate complex findings into actionable recommendations for Marketing, Growth, Product, and Finance stakeholders. The role requires strong SQL, BI dashboarding, marketing measurement, experimentation knowledge, and effective analytical communication, with AI tools used to accelerate reporting and analysis.

Designs and maintains healthcare analytics infrastructure, production data models, semantic layers, and transformation pipelines using advanced SQL and analytics engineering practices. Integrates payer claims, EMR, clinical, attribution, quality, and operational data. Performs ad hoc analysis, defines standardized healthcare metrics, validates data quality, optimizes queries, documents data flows, and partners with clinical, operational, IT, and payer stakeholders to support value-based care and enterprise reporting.

Designs scalable data models, optimized SQL queries, analytics infrastructure, semantic layers, and data applications. Partners with data science, product, engineering, and business teams to enable dashboards, self-service analytics, reporting, and enterprise data-driven decision-making. The role also establishes analytics engineering best practices, improves developer experience, and supports secure, compliant data warehousing.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, cloud data infrastructure, and complex data models. Supports analytics partners, develops data warehouse solutions, tunes SQL and database performance, troubleshoots production issues, and delivers end-to-end data engineering projects. The role collaborates with architects, analysts, developers, and business stakeholders, applies big data technologies, supports agile delivery, and mentors team members.

Own the customer experience data foundation by designing dbt models, pipelines, data quality tests, documentation, semantic layers, dashboards, and self-service datasets. Integrate support, KYC, withdrawal, CRM, workforce management, and market resolution data into the cloud warehouse. Identify recurring support issues, communicate findings to stakeholders, and partner with data engineering on infrastructure and governance.

Build Polymarket’s CX data and analytics function, defining service and customer experience metrics, creating dbt models and reporting, and delivering actionable insights to leadership. Lead Analytics Engineers, partner with central data engineering on pipelines and infrastructure, and develop measurement frameworks for AI automation. Apply forecasting and modeling to staffing, contact drivers, and AI deflection while communicating recommendations to executive audiences.

Own Carrum Health’s dbt data mart, semantic layer, client-facing exports, and data reliability standards. Lead, coach, and hire analytics engineers while establishing testing, documentation, freshness monitoring, metadata, and governance practices. Develop Hex as a trusted self-service analytics and AI interface with PHI controls, support the Databricks migration, and translate business needs into prioritized technical work. The role requires hands-on coding, strong dbt expertise, cloud data warehousing experience, and operational data ownership.

Designs and governs scalable dbt data architecture, analytics models, testing, documentation, metadata, and semantic-layer standards. Builds agent-safe infrastructure and self-service analytics experiences in Hex, improves data quality and reliability, and partners with engineering, data science, and business teams. The role also influences roadmap decisions, mentors teammates, and develops reusable tooling and frameworks for trusted, cost-efficient cloud data products.

Lead revenue analytics by developing business cases, dashboards, KPI frameworks, experiments, and automated analytics solutions. Partner with client success, marketplace, merchants, and cross-functional teams to evaluate performance, guide go-to-market decisions, quantify product value, and scale data-driven execution. Serve as the analytics subject matter expert while translating structured and unstructured data into actionable recommendations and compelling stakeholder narratives.

Lead end-to-end analytics for major platform partnerships, identifying growth opportunities and informing product, investment, risk, and pricing decisions. Conduct complex analyses, design experiments, build scalable data models, metrics, dashboards, and AI-assisted workflows, and translate findings into business recommendations. Partner cross-functionally with Product, Engineering, Finance, Revenue, and Strategic Partnerships while mentoring analysts and improving analytical practices.

Leads digital and marketing analytics for customer acquisition across multiple lending brands. Owns performance reporting, measurement frameworks, data governance, attribution and marketing effectiveness models, funnel analytics, experimentation, dashboards, and executive insights. Partners with Marketing, Risk, Finance, Operations, Technology, Engineering, and Data teams to optimize conversion, ROI, customer journeys, and data infrastructure. Requires advanced SQL, web analytics, statistical analysis, experimentation, and marketing measurement expertise.

Leads analytics, reporting, data modeling, automation, AI-agent development, and quality assurance for revenue cycle operations. Creates Power BI and Excel reports, analyzes complex healthcare and operational datasets, develops scalable warehouse models, and delivers actionable insights to leadership. Uses SQL, cloud data platforms, Git, Python, and AI-assisted tools to improve reporting efficiency, accuracy, and decision support.

Lead end-to-end COGS and gross margin analysis for hardware and software products, model cost-saving scenarios, answer ambiguous executive questions, partner with data engineering/finance/product, build reliable datasets and analytical frameworks, and present recommendations to C-suite stakeholders.

Leads analytics and data engineering for Sales and Revenue Operations. Builds reporting in Looker and Snowflake, manages CRM data flows into cloud warehouses, establishes transformation and modeling layers, and develops forecasts, scoring, churn, and expansion models. Supports territory, quota, pricing, and revenue-truth initiatives while presenting insights to finance and company leadership.

Analyze product, user, transactional, and repayment data to develop risk and underwriting models, reduce losses and fraud, monitor model performance, and guide product and growth strategy. Build Looker dashboards and LookML models, conduct segmentation and experimentation, measure CAC and LTV, and communicate actionable insights to product, engineering, marketing, finance, and leadership.

Own the analytical function for a business unit across growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, experiments, and analyses; generate actionable insights; communicate recommendations to leadership; and drive execution through measurable outcomes. The role requires independent problem-solving, strong analytical skills, persuasive communication, and ownership of ambiguous, high-impact initiatives.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; and drive recommendations through execution. The role requires strong first-principles thinking, independent data work, persuasive communication, stakeholder influence, and ownership of ambiguous, high-impact initiatives.

Leads data engineering, business intelligence, and analytics teams; oversees data pipelines, integration infrastructure, database architecture, CI/CD practices, and enterprise data platform strategy. Partners with business and technology stakeholders to define requirements, improve data capabilities, maintain Ubuntu-based services, and deliver reporting and KPIs to leadership. Responsible for team hiring, development, mentoring, operational health, and technical execution across analytics and data integration platforms.

Own GTM performance analytics, KPI and target setting, reporting, forecasting, compensation planning, and performance reviews across Sales and Post-Sales. Identify anomalies and optimization opportunities involving conversion, churn, productivity, pricing, packaging, segmentation, channel economics, and customer quality. Partner with Finance and GTM leaders to develop recommendations, lead strategic initiatives, and ensure data integrity while translating analysis into actionable business decisions.