Top Remote Senior Data Analyst Jobs

Own BI analytics using a modern data stack to generate insights, build dashboards and self-serve tools, partner with stakeholders, and deploy AI-driven reporting and natural-language query solutions.

Manage Medicare Advantage eligibility and benefit data: query Snowflake and SQL Server, support vendor data exchanges, troubleshoot production issues, create Python data scripts, validate and reconcile data, collaborate with SMEs, present analyses, and maintain documentation and reusable solutions.

Senior Data Analyst responsible for analyzing complex Medicare Advantage datasets, building scalable reports, dashboards, ETL/ELT pipelines, and analytics using SQL, Snowflake, and Databricks. Partners with stakeholders and vendors to translate findings into actionable recommendations, ensure data quality and compliance, mentor peers, and drive process improvements and automation.

Serve as a business-facing senior data analyst for Medicare Advantage supplemental benefits, building scalable reports, dashboards, ETL/ELT pipelines, and analytics using SQL, Snowflake, Databricks, and related tools. Partner with stakeholders and vendors to deliver insights, ensure data quality and compliance, mentor team members, and drive process improvements.

Serve as a Senior Data Analytics Consultant specializing in Dynatrace and observability to analyze digital experience data, produce dashboards and reports, advise 4–8 enterprise clients, present findings to technical and executive audiences, configure tooling, influence product roadmap, and support client enablement and account growth.

The Senior Data Analyst oversees performance reporting tools, automates data pipelines, conducts quantitative analyses, and communicates findings to various stakeholders.

Partner with People teams to turn HR and org data into actionable insights. Build complex queries, statistical analyses, dashboards, and a centralized people data repository. Drive data governance, data quality, and stakeholder communication to support hiring, retention, productivity, and workforce decision-making.

Design, implement, and validate data mapping, transformation, and integration solutions for payment integrity. Investigate and resolve complex data quality issues, create mapping specs and documentation, partner with stakeholders, and mentor analysts to improve data quality and automation.

Lead revenue analytics for GTM: own annual operating plan and weekly forecast, build propensity/churn/expansion models, architect the revenue data warehouse and identity resolution, define TAM/ICP, leverage AI to automate insights, and partner cross-functionally to maintain single-source-of-truth revenue metrics.

Design, build, and maintain data pipelines, dashboards, and predictive models. Analyze large datasets to generate actionable insights, collaborate with stakeholders, ensure data quality and governance, and document/share analytics best practices to support Marketing, Product, Operations, and Workforce teams.

Analyze portfolio-level organic search performance to identify trends and opportunities, build dashboards and benchmarks, define KPIs, evaluate strategy and product impacts, partner with Product/Data/Engineering, and translate insights into clear recommendations for technical and non-technical stakeholders.

Design, build, and maintain analytics and reporting for payment integrity post-pay reviews. Extract and consolidate large healthcare claims datasets, apply statistical analysis, develop dashboards and algorithms, and communicate findings to stakeholders. Support monitoring, tracking, and production deployment of analytics solutions.

Drive Data Governance requirements, analyze and validate data, collaborate with stakeholders, and maintain Data Governance artifacts using SQL and Informatica tools.

The Senior Data Analyst transforms data into insights to guide business decisions, conducts analyses, and improves data collection systems.

Partner with business and product stakeholders to design, analyze, and interpret A/B tests and causal analyses, build Tableau dashboards and reports, present findings to non-technical audiences, and translate insights into actionable business recommendations.

Build and maintain scalable monitoring solutions and dashboards to track production, campaign, and operational data. Analyze large-scale datasets to detect anomalies, perform root-cause investigations with Engineering and Production Operations, and deliver actionable insights to improve platform reliability and operational efficiency.

Lead cross-functional analyses and build scalable reporting for go-to-market teams. Develop and automate data pipelines, create interactive dashboards, improve data quality, and translate complex findings into actionable recommendations for stakeholders and executives.

Lead marketing and digital analytics for financial institutions: analyze large datasets, build attribution and customer journey models, optimize campaigns via A/B testing, create executive dashboards and automated reports, and partner cross-functionally to improve customer experiences and marketing ROI.

Analyze PB-scale trading, transactional, operational and customer data; build visualizations, ML models and experiments; translate findings into actionable recommendations for trading, operations, product and executives in a fast-paced environment.

Analyze and consolidate disparate data sources, document functional specifications, review logical and conceptual data models, gather business requirements, validate implementations, support production deployments, investigate data quality issues, and coordinate with technology leads to resolve data completeness and accuracy gaps.

Lead reconciliation data analysis for cash, stock and position records. Gather requirements, document functional specs, validate solutions, support production deployments, investigate data quality and work with technology leads to ensure data completeness and lineage.

Analyze C&DS project data to produce actionable insights. Build data pipelines, reports, visualizations and predictive/prescriptive models. Collaborate with IT, data governance, and business stakeholders to enable data-driven decision-making, support reporting needs, and ensure data quality and access.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.

Design, analyze, and document data products and technical specifications. Perform large-scale data profiling and SQL analysis, support data governance, partner with stakeholders to remediate issues, and help implement data quality, lineage, and ingestion standards.

Analyze customer journey data to identify conversion drivers and friction points. Collaborate with teams to optimize product strategies using data insights.