Top Senior Data & Analytics Jobs in Charleston, SC

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Support data-driven operations by sourcing, integrating, validating, and transforming data; build and maintain dashboards, agency scorecards, and reports; analyze KPIs to identify improvement and cross-sell opportunities; measure campaign effectiveness; ensure data quality; partner with stakeholders and technology teams; support monthly reviews and ad-hoc analytics requests.

Own slide-grade analytics for PE and executive audiences by transforming Salesforce and BI outputs into polished board packs, causal commentary, reusable templates, and executive narratives. Attribute pipeline variance, produce funnel and revenue analytics, train client teams, and ensure deliverables are modular, PE-ready, and maintainable across engagements.

Client-facing Analytics Manager who architects large-scale digital analytics (GA4/Adobe), audits implementations, builds measurement frameworks, and leads GEO (Generative Engine Optimization) test & learn programs. Uses AI tools (Claude) to automate workflows, mentor teammates, manage projects, and translate data into strategic client recommendations.

Build, optimize, and maintain web analytics data and reporting. Deliver weekly reports and insights, perform deep-dive analyses, recommend website optimizations, partner with paid digital and email teams, and ensure accurate Adobe Analytics implementations.

Lead scaling of Arbital's AWS/Databricks lakehouse and Spark pipelines, implement actuarial healthcare calculations, own Airflow orchestration and CI/CD, ensure data quality and HIPAA-compliant PHI handling, partner with data scientists to deploy ML models, and set platform architecture and engineering standards.

Design, build, and maintain scalable backend systems and data pipelines to generate point-of-care insights; collaborate with product and engineering stakeholders; mentor junior engineers; ensure solutions are secure, performant, maintainable, and support analytics and unified metrics collection.

Design, build, and deliver a global analytics platform: manage data collection and pipelines, develop visualizations and reports, create semantic models, tune performance, collaborate with IT and business stakeholders, and implement governance for analytics products.

Lead and scale companywide analytics for a DTC marketing business: own marketing measurement, financial forecasting, retention/LTV modeling, data architecture, reporting, and the analytics team. Set analytics strategy, oversee data engineering roadmap, build dashboards, apply statistical and ML methods, and translate insights into executive-level decisions.

Lead measurement strategy for a book of clients using statistical modeling (MMM), incrementality experiments (MMT), and customer analysis. Build insights from large datasets with R, Python, and SQL, present actionable recommendations to clients, mentor analysts, improve measurement processes, and leverage Nova and AI to optimize deliverables and impact business goals.