Top Remote Data & Analytics Jobs

Develop and maintain Power BI reports, dashboards, apps, and reusable semantic models using SQL, DAX, and Power Query. Own day-to-day Power BI platform operations, including workspaces, publishing, licensing, refreshes, gateways, permissions, and content lifecycle. Translate stakeholder requirements into decision-ready reporting experiences, validate data and calculations, support testing and releases, troubleshoot issues, and coordinate with engineering and security teams. Coach report authors and promote governed, reliable self-service analytics.

Analyze complex datasets, validate data quality, identify trends, and deliver actionable business insights. Develop reports, dashboards, KPIs, and scalable analytics solutions using SQL, Azure services, Excel, and visualization tools. Collaborate with business and technical teams on requirements, testing, issue resolution, stakeholder communication, and data-quality improvements. Track project risks and deliverables while supporting automation and AI-assisted analytics.

Own end-to-end analytics for subscription products and web funnels, including KPI definition, dashboards, funnel and cohort analysis, payment performance, user behavior, experimentation, and anomaly monitoring. Build reliable SQL data models and maintain event taxonomies and tracking. Partner cross-functionally with Product, Marketing, Engineering, Finance, and BI to improve conversion, retention, revenue, and LTV. Mentor analysts and communicate actionable recommendations to stakeholders.

Analyze and model property catastrophe reinsurance treaties using catastrophe modeling platforms. Prepare and validate exposure data, execute models, interpret risk metrics, develop scenarios, and identify portfolio risk drivers. Support underwriting, actuarial pricing, placement, renewals, and management decisions through clear reporting. Participate in model validation, calibration, research, and development while collaborating with underwriters, actuaries, and other stakeholders.

Leads advanced quantitative market research from client discovery through study design, analysis, interpretation, and recommendations. Establishes methodological and data-quality standards; designs segmentation, MaxDiff/conjoint, pricing, experimental, and longitudinal studies; evaluates sampling, weighting, statistics, and respondent quality. Leads senior client discussions, translates findings into business implications, partners with technical and execution teams, evaluates emerging research methods, and mentors researchers and analysts.

Owns and maintains organizing data systems, voter-contact tools, and organizational databases. Ensures data integrity through quality assurance, audits, standardized processes, documentation, and permission management. Supports organizers by troubleshooting tools, managing data requests, providing training, and improving workflows. Writes SQL queries in BigQuery, develops reports and visualizations, supports integrations, and collaborates with engineering and analytics teams on data models and priorities.

Organise and curate large amounts of raw material: extract key points from transcripts, collate and maintain clean spreadsheets, tag and categorise links and documents, and create findable information structures following or suggesting systems.

Leads end-to-end data and analytics architecture engagements, specializing in Microsoft Fabric platform design, implementation, modernization, migration, governance, integration, semantic models, reporting, performance, and cost optimization. Partners with clients and internal teams to translate business needs into scalable solutions, guides technical delivery, supports pre-sales pursuits, and mentors data professionals. The role is remote but requires travel four to six times annually and residence in an approved state.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Build and maintain reusable analytical data products, canonical entities, data marts, semantic layers, and governed metrics across business domains. Establish modeling, documentation, quality, lineage, and change-management standards; improve data reliability, self-service analytics, and production governance. Partner with Data Platform, Trust, Data Science, Applied AI, and business stakeholders on orchestration, observability, access controls, feature tables, model outputs, and shared metric definitions. Remain hands-on with SQL, Python, testing, code reviews, documentation, and technical coaching.