Top Remote Data & Analytics Jobs

Manages HR data, analytics, reporting, dashboards, pipelines, integrations, and automation. Supports compensation, performance management, workforce planning, HRIS maintenance, and employee listening initiatives. Builds data models and AI integrations, audits data integrity, develops actionable insights, and presents findings to HR and executive leadership. Requires strong compensation expertise, advanced Excel, HRIS and BI experience, scripting, data modeling, and handling confidential information.

Own the operations analytics portfolio: prioritize data and AI opportunities, translate insights into decisions, drive adoption into workflows, specify hand-offs to central data teams, and measure success by operational outcomes and sustained adoption.

Develop scalable analytics assets and serve as the Power BI/Microsoft Fabric expert. Build and publish trusted datasets, semantic models, and dashboards; author Databricks workflows and production SQL; apply R for statistical analysis; ensure governance, versioning, and reproducibility; partner with stakeholders and Data Engineering to deliver high-impact insights, define KPIs, and communicate executive-ready recommendations.

Research and verify mental health service directory records, including addresses, hours, contacts, costs, insurance, and eligibility tags. Apply screening criteria, cite supporting evidence, identify new local services, and flag records that do not fit the data model. The role requires careful spreadsheet organization, adherence to written procedures, accurate source checking, and clear communication with service organizations.

Analyze behavioral, transactional, and interaction data to identify fraud patterns and emerging threats. Develop monitoring frameworks, dashboards, and mitigation solutions; evaluate AI fraud-review decisions; and translate findings into prompt, rule, workflow, and machine-learning feature improvements. Collaborate with Fraud Operations and Machine Learning teams, participate in code reviews, and mentor junior analysts. The role requires strong SQL and Python skills, statistical analysis expertise, business judgment, and fraud analytics experience.

Design, develop, and maintain data pipelines, ETL processes, integrations, data infrastructure, and complex data models. Build and support data warehouses and analytics solutions using SQL, PL/SQL, Oracle, Snowflake, and cloud technologies. Collaborate with analysts, architects, developers, data scientists, and business stakeholders throughout the delivery lifecycle. Provide production support, troubleshoot issues, tune database performance, and contribute to data-driven reporting and AI tools.

Lead analytics support for Fetch’s Food and Beverage vertical, serving as the primary analytics partner for Sales and client stakeholders. Own sub-vertical partnerships, manage analytic capacity, answer senior-level client questions, and guide campaign strategy. Use SQL to manipulate data, develop actionable insights, support QBR storytelling, and communicate recommendations to technical and non-technical audiences. Represent analytics cross-functionally and automate insights using AI-enabled tools.

Supports dataset collection, cleaning, analysis, visualization, reporting, A/B testing, and performance tracking for education products. The intern will identify trends in student, educator, and product usage data, build dashboards, document analytical frameworks, and communicate insights to technical and non-technical teams. This is a paid, remote 10-week internship with a two-day in-person experience at Amplify headquarters in Brooklyn.

Analyze Amazon Marketing Cloud, advertising, retail, and commerce data to develop custom queries, reporting, dashboards, tests, forecasts, and actionable client recommendations. Partner with commerce and client teams, explain technical findings, validate data quality, document methodologies, and improve analytics workflows. Support platforms including Amazon, Walmart Connect, and Scintilla while using SQL, BI tools, and optionally Python or R for custom analysis and lightweight modeling.

Performs structured data analysis under senior guidance to solve customer problems, identify patterns and anomalies, test hypotheses, and develop reusable data science components. Collaborates with engineering, mission operations, solution delivery, and customers to scope requirements, improve processes, and translate analytical findings into actionable business decisions. The role requires strong communication, coachability, analytical coding ability, and willingness to travel up to 50%.