Top Analytics Jobs in Charleston, SC

Analyze and forecast financial, economic, and market data to support strategic and operational decisions. Develop premium variance reports, set monthly revenue targets, perform A:E analyses, expand explanatory driver interfaces, ensure data integrity and controls, troubleshoot anomalies, and collaborate with actuarial and finance partners.

Lead a team to analyze sales data, build and maintain Power BI and Excel reporting, leverage AI and automation for pipeline and lead scoring, align cross-functional GTM strategies, ensure data quality, and deliver actionable, executive-ready insights to drive sales performance and quota attainment.

Lead BI developer who owns reporting infrastructure end-to-end: SQL-based data extraction from messy sources, LookML/BI model development, dashboard standardization, stakeholder partnership, and performance marketing optimization to drive actionable insights and business outcomes.

Lead analytics function: deliver product, marketing, and business analytics; build and scale the analytics team; partner with CEO to drive growth; be a hands-on manager and top analyst; hire and manage teammates.

Lead design and implementation of tracking with GTM, analyze user behavior in GA4, build BigQuery queries and dashboards, define KPIs with cross-functional teams, troubleshoot tagging/data issues, and perform deep-dive conversion and journey analyses to optimize web and mobile performance.

As a Senior Analytics Engineer, you will own Solv's analytics stack, focusing on data pipeline efficiency, customer-facing analytics, and AI-powered tools, working with internal teams to improve metrics, dashboards, and data visibility.

The Director of Product Analytics leads the product analytics strategy, develops a team of analysts, drives insights for product decisions, and oversees experimentation across products to enhance growth outcomes.

The Manager of Analytics oversees a team to deliver impactful analytics and insights, supports business decisions, and integrates AI applications in analytics. They also manage testing portfolios and develop actionable insights for marketing strategies.

The Senior Consultant will lead the design and implementation of data solutions using Microsoft Fabric, Power BI, and Databricks to enhance analytics capabilities for clients. They will engage with stakeholders to define data strategies and deliver high-quality consulting services across multiple projects.

Build and own the reporting and insight layer: write optimized Redshift queries, build dashboards, monitor model feedback signals, triage and prioritize investigations for data science, maintain ETL pipelines ingesting semi-structured data, and enable Customer Success with clear metrics and onboarding checks. Move into model validation and automation to scale analytic workflows.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.

Provide enterprise analytics expertise supporting reports, dashboards, and analytic tools. Coordinate and complete analytical requests via ServiceNow, resolve complex technical issues, participate in projects and decision-making, transfer knowledge to peers, and ensure standardized development and high customer service.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

The Marketing Analytics Manager role involves managing marketing performance metrics, analyzing campaign performance, and collaborating with teams to optimize marketing investments and conversions.

The role involves designing and maintaining data models and pipelines, collaborating with teams to drive insights, and building dashboards for analytics and reporting.

This role involves conducting advanced data analysis to drive strategic decisions, defining metrics, collaborating with teams, and improving performance through insights.

The Senior Data Analytics Engineer analyzes and delivers business intelligence solutions, builds data pipelines, and collaborates with clients to meet their data-driven needs.

As the Director of Analytics, you'll lead analytics for MyPrize, optimizing performance across teams, developing reporting tools, and informing data infrastructure.

The Analytics & Google Tag Manager Specialist is responsible for migrating clients from Google Analytics 3 to Google Analytics 4, setting up GA4 and GTM, and linking accounts to Google Ads.

The Associate will generate insights, analyze data for better decision-making, enhance operational dashboards, and collaborate across teams to improve efficiency and report to leadership.

The Senior Manager, Analytics & Insights will support sales and operational teams through advanced analytics and data-driven insights, focusing on GTM strategies and analytics infrastructure.

Lead the People Analytics function by developing AI tools, predictive models, and dashboards to enhance HR decision-making and employee experience.

Voltus is seeking a Sales Technical Analytics Intern to develop data models, create reporting tools, and maintain data quality for the Sales organization, focusing on data pipelines and analytics.

The Analytics Implementation Manager will lead the implementation and validation of analytics infrastructure, ensuring measurement accuracy and reliability across client accounts. Responsibilities include deploying tagging solutions, managing data layers, coding in JavaScript, and collaborating with cross-functional teams to troubleshoot and enhance performance tracking.

Lead design and expansion of a unified analytics data platform integrating Medicare claims, clinical systems, and vendor feeds. Implement AI-augmented data engineering (LLM coding assistants, automated QA, autonomous data-quality agents), build validation/reconciliation/audit infrastructure, partner with analytics teams for reporting and modeling needs, and drive large-scale analytics platform and web app implementation while collaborating with IT on security and deployment standards.