Top Senior Data & Analytics Jobs in Greensboro, NC

Lead enterprise data architecture across AWS and Databricks Lakehouse: design scalable data models, ensure governance and data quality, mentor teams, evaluate and adopt data technologies (Delta Lake, Unity Catalog), oversee large projects and deliverables, and incorporate generative AI to accelerate data modeling and delivery.

Owns and leads commercial master data management across HCP, HCO, payer, patient, product, and organizational domains. Establishes governance frameworks, stewardship programs, data quality standards, matching and survivorship rules, and trusted golden records. Manages commercial data platforms, integrations, analytics datasets, releases, vendors, and offshore teams. Writes SQL, builds validations and data pipelines, troubleshoots integrations, supports commercial launches, and drives operational improvements across pharmaceutical commercial systems.

Support Numerator clients through ad hoc analyses, consumer-panel insights, platform guidance, reporting, and recommendations that improve sales and marketing effectiveness. The role requires understanding syndicated and household panel data, business intelligence platforms, client relationship management, presentations, and data-driven problem solving. It also involves developing expertise in Numerator solutions and sharing consumer behavior findings through industry content, forums, whitepapers, and blogs.

Designs and implements Google Cloud database migration strategies, including architecture, data models, database objects, pipelines, orchestration, security, and data transfers. Advises customers on Cloud Spanner and Cloud SQL implementations, translates SQL and stored procedures, configures Google Cloud components, performs performance testing, resolves performance issues, and delivers technical workshops and architectural recommendations.

Develop and optimize marketing reports, monitor product and advertising campaign KPIs, integrate and visualize data, investigate performance deviations, analyze campaigns, and recommend improvements. The role requires strong Google Sheets skills, basic SQL, A/B testing, mathematical statistics, and English proficiency. Python, web analytics, Google Ads, and BI experience are preferred. This is a fully remote position.

Leads the design and implementation of AI-powered data pipelines, enterprise feature stores, analytics-ready repositories, and self-service data access layers. Builds automated feature engineering, data quality, observability, and governance capabilities for large-scale data science workloads. Partners with Enterprise IT, establishes data availability and quality SLAs, integrates structured and unstructured data sources, and leads teams delivering scalable analytics infrastructure.

Reviews AI-generated documents, spreadsheets, and slide decks for accuracy, rigor, presentation quality, and domain-specific compliance. Evaluates outputs using quality rubrics, identifies factual and aesthetic errors, and provides structured written feedback. The role requires deep expertise in data quality and CRM operations, strong English fluency, and proficiency with Microsoft Office and Google Workspace. It is a fully remote, flexible independent-contractor engagement.

Analyzes, cleanses, validates, and prepares operational datasets for mailing, email, reporting, billing, and processing activities. Develops Databricks queries and Alteryx workflows to automate processes and improve data quality. Performs audits, reconciliations, root-cause analysis, and corrective actions while maintaining governance and security compliance. Provides actionable insights, mentors junior analysts, communicates findings, and leads continuous improvement initiatives.

Own Kepler’s transaction data feed end-to-end, ensuring accuracy, improving methodologies, developing QA frameworks, solving complex analytical problems, and prototyping enhancements. Analyze massive datasets, automate workflows with AI, evaluate client-facing AI outputs, address high-complexity customer questions, and collaborate with Research, Product, Data Science, and Engineering teams.

Analyze platform, product, customer, advertising, and operational data to generate actionable insights. Build dashboards, reporting frameworks, automated workflows, and scalable analytics pipelines. Evaluate experiments and analytical methodologies, improve forecasting and data accuracy, and partner with product, engineering, marketing, revenue, and leadership teams to influence strategy, customer experience, and platform growth.