Top Remote Data & Analytics Jobs

Analyzes and validates data to create dashboards, reports, notebooks, visualizations, and reusable analytical workflows. Partners with business, technical, and customer stakeholders to define questions, improve datasets, communicate insights, and support operational and revenue decisions. Uses SQL, Databricks, Python, cloud platforms, version control, and automation practices. Maintains data quality, documents assumptions, manages multiple analytical projects, and travels occasionally to lottery and corporate locations.

Manages enterprise data governance and privacy compliance programs, including GDPR and CCPA adherence, data subject rights processes, governance controls, privacy documentation, compliance metrics, and continuous improvement. Coordinates with business, legal, compliance, security, and technology stakeholders; maintains process documentation; monitors risks and response timelines; supports automation, reporting, awareness training, and audit readiness across global operating units.

Lead marketing analytics: own reporting frameworks, dashboards, and data quality; monitor KPIs; run A/B tests and experiments; build attribution, forecasting, segmentation, and budget-allocation models; automate workflows and drive continuous measurement improvements.

Own advanced analytics for Airbnb product teams, using large datasets, statistical modeling, experimentation, causal methods, forecasting, and metrics development to guide product and business decisions. Build reporting and analytical tools, collaborate cross-functionally, automate repetitive work with AI, and present actionable insights to senior stakeholders. The role supports Guest App or API initiatives, including promotions and marketplace optimization.

Analyze product telemetry, user signals, performance, behavior, feature adoption, and product metrics. Develop recurring reports, dashboards, and visualizations, investigate issues and trends, and provide insights supporting product initiatives and rapid iteration. Collaborate with Product Managers, Applied Scientists, Engineers, and stakeholders across China and the United States.

Leads enterprise Business Intelligence strategy, architecture, governance, security, and self-service analytics adoption. Architects solutions across Power BI, Tableau, SAP BusinessObjects, and Databricks; establishes semantic models, data standards, DevOps, CI/CD, and reporting performance practices. Partners with executives and technical teams to modernize analytics platforms, reduce manual reporting, and enable AI-ready decision-making. Mentors BI professionals and drives enterprise data literacy and analytics transformation.

Collects, enters, validates, and maintains property-level ESG and utility data for assigned client portfolios. Coordinates with property managers, utility companies, and vendors; updates ENERGY STAR Portfolio Manager; monitors data quality and timeliness; resolves routine discrepancies; supports onboarding, training preparation, reporting workflows, and property offboarding. The role emphasizes accurate execution of established processes, documentation, communication, and escalation of exceptions.

Analyze market and live data to set dynamic ticket prices using Uptick, build strategic pricing recommendations, communicate with clients about strategy and performance, and collaborate with BI and engineering to improve product performance.

Analyze healthcare operations data to identify bottlenecks, model advocate capacity and supply-demand, build scalable Hex dashboards, refine SQL and dbt data transformations, and translate complex findings into operational recommendations. The role partners with Operations, Engineering, Product, Marketing, and Finance to improve workflows, reporting, resource allocation, and business outcomes across the patient and advocate journey.

Leads LTK’s Data Engineering team, owning technical direction, architecture, pipelines, data products, governance, quality, reliability, and platform modernization. Manages and develops engineers, drives roadmaps and complex initiatives, partners with cross-functional stakeholders, and balances scalability, security, performance, cost, and business priorities. Establishes engineering standards, improves operational practices, manages risks and dependencies, and builds trusted, reusable data capabilities supporting analytics, reporting, products, and AI use cases.