Top Remote Data & Analytics Jobs in Greensboro, NC

Serves as a strategic analytics partner to Marketing, owning funnel, attribution, ROI, account scoring, experimentation, and pipeline analytics. Builds scalable datasets, dashboards, and quality controls while connecting customer, campaign, product usage, CRM, and revenue data. Delivers executive-ready reporting, QBR insights, and recommendations that improve conversion, marketing investment effectiveness, account-based marketing, product adoption, and pipeline growth.

Design and maintain scalable ETL/ELT pipelines, analytical dashboards, and reporting tools supporting clinical, research, and operational priorities. Write production-quality Python, R, and SQL; develop data architecture; partner with data scientists to operationalize analytics; integrate AI-enabled workflow improvements; establish engineering standards; and mentor junior team members.

Supports, maintains, troubleshoots, and optimizes production Databricks applications, Spark jobs, SQL queries, Delta Lake pipelines, clusters, workflows, and data quality processes. Responsibilities include platform configuration, observability, documentation, deployment support, technical testing, finance data engineering, stakeholder coordination, and incremental improvements. The role also contributes to reporting enablement, operational readiness, process improvement, and on-call support during EST business hours.

Support marketing analytics by analyzing digital channel performance, building and maintaining KPI dashboards, ensuring data accuracy, and collaborating with cross-functional teams to turn insights into actionable recommendations.

Analyze performance marketing data, including traffic quality, unit economics, reporting, attribution, A/B testing, forecasting, and monetization. Build dashboards, anomaly alerts, and data models using BigQuery, dbt, and Tableau. Partner strategically with performance teams to identify growth opportunities, improve channel evaluation, and guide scaling decisions through SQL, Python, and statistical analysis.

Analyze marketing performance across paid media, CRM, email, and customer engagement channels. Prepare recurring reports, consolidate data into actionable insights, support client recommendations and optimization strategies, and perform quality checks on reports and datasets. Collaborate with strategy, client services, media, and analytics teams while using AI tools responsibly to improve reporting efficiency.

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.

Owns analytics datasets and data products for Netflix’s cloud games infrastructure. Builds scalable pipelines, data models, metrics, dashboards, tooling, and self-service assets from massive telemetry data. Partners with infrastructure engineering, data engineering, and data science teams to improve quality of experience, capacity, and utilization insights. Responsibilities include telemetry and logging, visualization, trustworthy documentation, scalable data solutions, and independently prioritizing high-impact investments.

Investigate offline and direct mail channel performance, attribution, vendor data, and measurement methodologies. Build response, lift, allocation, and regression models; manage direct mail testing data pulls and mail merges; create KPI dashboards; and communicate actionable recommendations to marketing stakeholders. The role requires proactively identifying opportunities, validating data and vendor claims, and translating complex analytical findings into business decisions.

Design and lead scalable data engineering solutions across Databricks and Microsoft Azure. Define enterprise data modeling standards, architect pipelines and integrations, establish engineering best practices, troubleshoot complex platform issues, and guide modernization and cloud migration initiatives. Provide technical leadership and mentorship while ensuring data platforms meet security, quality, governance, reliability, performance, and maintainability standards.