Top Remote Senior Data Analyst Jobs

The role involves improving transaction monitoring rules, analyzing risk patterns, and building dashboards for compliance while collaborating with investigations and compliance leadership.

Analyze complex financial and business data to guide planning, forecasting, investment decisions, and strategic growth. Partner with FP&A, Accounting, RevOps, Product, Marketing, Data Engineering, and analytics teams; automate recurring workflows; build self-service dashboards and data assets; improve analytics infrastructure; and communicate actionable insights to technical and non-technical stakeholders.

Develops scalable data models, dashboards, reports, and analytical solutions that support business decisions. Partners with stakeholders to translate requirements, ensures data accuracy and usability, provides technical guidance to end users, documents architectures and processes, and communicates findings to technical and non-technical audiences. The role requires advanced Spotfire, SQL, and business intelligence experience, plus familiarity with modern data platforms and AI-assisted development tools.

Lead ownership of client billing data and infrastructure for Finance: run reconciliations, investigate billing issues, build BI dashboards, automate workflows with SQL/Snowflake, monitor revenue leakage, and support Billing 2.0 UAT while training Finance on reporting and Looker standards.

Own finance, accounting, and revenue operations data pipelines; develop revenue forecasts; conduct analysis; and build scalable SQL solutions, dashboards, and reports. Partner with executives and cross-functional stakeholders to translate complex financial data into accurate insights and recommendations. Maintain rigorous data validation, documentation, QA, and process improvements while managing competing priorities.

Develop and maintain data models, semantic layers, dashboards, and Python- and SQL-based reports for claims analytics. Analyze operational performance, investigate root causes, and provide actionable recommendations to leadership. Collaborate with engineering, operations, and other stakeholders to translate source systems into standardized data models and deliver effective analytical solutions. Communicate findings through clear writing and presentations while helping shape a growing data team.

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.

Creates, analyzes, edits, and maintains GIS data supporting fiber and HFC network planning, design, construction, and regulatory map updates. Uses ArcGIS and ArcFM to manage geodatabases, spatial datasets, parcel data, route assets, and network schematics. Conducts map research, interprets technical records, supports GIS projects, develops mapping standards, and responds to internal requests. Collaborates with legal, market expansion, engineering, construction, and design teams while ensuring geospatial data quality and alignment with GIS best practices.

Own analytics for Customer Success and Operations by translating ambiguous business questions into metrics, reports, dashboards, and PRDs. Partner with senior leaders, CSMs, Analytics Engineers, and Operations teams to improve retention, permit turnaround, and organizational scalability. Build self-service dashboards, develop dbt models, document analytics assets, prioritize high-impact requests, and use LLM tools to accelerate SQL, reporting, and data-model development.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Analyze and design enterprise healthcare data solutions using Snowflake and advanced SQL. Perform data profiling, reconciliation, anomaly detection, source-to-target validation, and quality testing across claims, membership, provider, and health plan data. Support data architecture, dimensional modeling, governance, metadata, lineage, cataloging, and MDM initiatives. Prepare data for AI and advanced analytics, translate business requirements into solutions, resolve data issues with stakeholders, and communicate actionable insights while following security and HIPAA requirements.

Leads Gartner research and thought leadership on data management, governance, and AI. Develops actionable insights, analyzes market trends, advises executive clients, delivers presentations, supports sales and conferences, reviews content, improves research processes, and mentors analysts. Requires deep expertise in data and analytics, AI implementation, data security, data value creation, executive communication, research, and strategic analysis. Remote U.S. role with travel up to 25%.

Lead data governance for a major enterprise platform migration: liaise with stakeholders, profile and map data, define standards and business rules, validate ETL/MDM mappings, document metadata and lineage, and support data quality and governance processes throughout migration and post-implementation.

Own end-to-end analytics across marketing, revenue, product, and partnerships. Build reliable data models and semantic layers in a lakehouse, maintain dashboards and monitoring, investigate anomalies, conduct experimentation, and translate complex analyses into recommendations for senior stakeholders. The role requires advanced SQL, Python, data modeling, stakeholder communication, autonomy, and mentoring, with responsibility for improving data quality and building analytical standards in a high-growth startup.

Supports advanced provider data management by analyzing healthcare data, improving workflows, maintaining accurate provider records, and applying contracting, network management, and credentialing rules. Leads cross-functional projects, gathers requirements, develops specifications, communicates metrics and insights, supports system adoption, trains users, and performs bulk database updates while ensuring data integrity.

Analyze claims data using Python, SQL, dbt, PostgreSQL, Looker, and Excel; build ETLs, data models, reports, and visualizations; lead customer calls; provide technical recommendations; clarify requirements; and communicate precise insights while collaborating with engineering and operations teams.

Build and maintain trusted Finance data products, including data models, transformations, pipelines, semantic models, dashboards, and analytical tools. Translate FP&A requirements into scalable solutions, validate and reconcile outputs, resolve data quality issues, and document lineage and business rules. Use SQL and Python for automation and advanced analytics supporting forecasting, revenue, customer, portfolio, and operational analysis. Partner with shared data teams and stakeholders while enabling self-service reporting and executive decision support.

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.

Leads integration of healthcare payer feeds and internal enterprise data into WellBe’s data platforms. Responsibilities include source-to-target mapping, transformation logic, data validation, normalization, quality monitoring, governance, metadata and lineage management, and partnership with data engineering. The role translates clinical and business requirements into technical specifications, leads customer discovery sessions, supports analytics and reporting, and drives multiple concurrent integration initiatives through agile delivery.

Performs independent quality assurance and validation across enterprise data platforms, warehouses, integrations, reporting solutions, and business processes. Responsibilities include data quality analysis, test strategy and automation, regression and release validation, incident investigation, root cause analysis, requirements traceability, governance, regulatory validation, and monitoring data quality metrics. The role partners with engineering, business, compliance, and operational teams while improving enterprise data integrity, reliability, and governance.

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.

Lead analytics strategy for federal programs: perform statistical modeling, forecasting, and data mining; build dashboards and automated workflows; ensure NIST/FISMA compliance and data quality; present findings to government stakeholders; mentor junior analysts and manage analytics workstreams.

Owns production data systems end to end, including data models, ETL and API integrations, applications, platform maintenance, data quality, governance, and technical debt. Supports OPSDB and exploration operations, improves reliability and alerting, documents systems, collaborates with cross-functional and nontechnical stakeholders, and mentors junior analysts. The role also supports procurement and inventory initiatives, works with geologists, and requires occasional international travel to exploration sites.

Build reliable data models and semantic layer for AI-powered analyses. Support Product, Finance, and Customer Success with SQL-based analytics, BI dashboards, and stakeholder storytelling. Standardize data team processes, prioritize work, and collaborate across company to enable data-informed decisions.