Top Senior Data & Analytics Jobs in Providence, RI

Designs and maintains healthcare data pipelines, ETL workflows, databases, data models, and analytical frameworks. Analyzes claims and payer data to identify trends and insights, while ensuring data quality and integrity. Creates reports, dashboards, and visualizations for nontechnical stakeholders and collaborates with healthcare policy experts, analysts, and other stakeholders to translate requirements into data solutions. The role also improves data infrastructure and analytics capabilities.

Design, build, and maintain scalable ETL/ELT pipelines and analytics-ready data models using Python and SQL. Implement GitLab CI/CD, develop dashboards in Tableau/Power BI, perform data cleaning and analysis, support modeling and AI initiatives, document metadata, and mentor junior staff while collaborating with cross-functional teams to deliver actionable insights.

Administer, optimize, secure, and automate cloud SQL, NoSQL, and graph database environments. Responsibilities include performance tuning, backups, disaster recovery, replication, migrations, access controls, monitoring, cost optimization, compliance, documentation, and incident troubleshooting. The role integrates databases with data platforms, pipelines, applications, and analytics tools while collaborating with engineering, architecture, security, and application teams. Candidates support AWS and Snowflake environments, modernization initiatives, audits, and Public Trust requirements.

Lead the elicitation, documentation, and governance of business and data requirements for cloud-based data products. Drive source-to-target mappings, collaborate with engineers and architects, support testing and UAT, produce Agile artifacts, contribute to metadata ingestion, and ensure audit-ready, compliant reporting and analytics for the CMM program.

Build and maintain scalable data pipelines, warehouse models, metrics, and data quality systems for Airbnb’s AirCover programs. Reconcile external partner data, publish analytics-ready datasets, improve pipeline observability and reliability, and collaborate with engineering, product, finance, operations, analytics, and data science stakeholders. Contribute to data engineering standards and foundational systems supporting reporting, experimentation, claims operations, financial reconciliation, and expansion into new markets.

The Lead, Advanced Analytics, Acquisition will analyze data, define metrics, conduct A/B tests, and collaborate with teams to drive growth and insights.

Support an insurance actuarial department by cataloging and classifying data, maintaining master data mappings and schemas, auditing data quality, enforcing governance policies, and ensuring regulatory compliance. Collaborate with technical and business stakeholders, conduct Alation and Excel training, and lead data governance projects aligned with organizational objectives.

Lead Business Analyst for Payment Integrity responsible for analyzing client data, capturing business requirements, designing product solutions, supporting bids, documenting processes, driving implementations, and delivering KPI-driven insights. Works cross-functionally with sales, marketing, finance, and delivery to build business cases and improve claims auditing and analytics outcomes.

Provide insights and recommendations to improve end-to-end purchase customer journeys across web and app for B2C and B2B. Lead A/B testing and experimentation, ensure accurate data collection and impact measurement, build self-service reporting and visualizations, query and process data, and present findings to stakeholders and senior leadership.

Own high-impact data products, ingestion pipelines, and granular data feeds end to end. Analyze massive datasets to answer client and stakeholder questions, ensure data accuracy, and derive actionable insights. Improve products and pipelines by developing methodologies, scaling third-party datasets, and creating data modules. The role requires clear communication, attention to detail, methodical problem solving, prioritization, experimentation, and strong ownership. Training includes SQL, PySpark, Python fundamentals, and internal tools.

Own and improve high-impact data products, ingestion pipelines, and granular data feeds. Analyze large datasets to answer client and stakeholder questions, ensure data accuracy, and derive insights. Collaborate with clients and internal teams, develop new methodologies and features, and scale third-party datasets. The role includes training in SQL, PySpark, Python, and internal tools.

Leads Commercial Markets Operations intelligence, business intelligence, analytics, reporting, dashboards, and strategic information initiatives. Manages analysts, develops performance metrics, oversees operational reporting, supports executive decision-making, and drives process improvement. The role also manages resource planning, budgets, stakeholder alignment, SharePoint stewardship, and adoption of innovative information solutions across Sales Operations and Commercial Markets.

Lead Workday reporting strategy for higher education: design Advanced, Matrix, Composite, and Dashboard reports; implement row-level security and role-based access; partner with HR/Finance/Payroll/Institutional Research; support regulatory and executive reporting; manage testing, validation, and user training while ensuring governance and data standards.

Design, build, and maintain Power BI dashboards and data models using Snowflake and other cloud sources. Collaborate with data engineers/ETL teams, gather stakeholder requirements, implement DAX and Power Query transformations, ensure data governance and performance, and deliver analytics within Agile processes.

Partner with customers and sales to design and present scalable Databricks Lakehouse architectures, build reference architectures and demos, consult on big data, ETL, and ML projects, validate integrations, and drive technical wins and adoption across accounts.

Design, optimize, and scale Snowflake-based data architecture and pipelines. Implement data observability, operationalize MLOps, build semantic enterprise models, and deploy AI-assisted agents and automation for production analytics and commercial data products. Share cross-functional ownership for pipeline resilience, monitoring, and model lifecycle management.