Top Analysis Reporting Jobs in Charleston, SC

Lead the design and implementation of algorithmic trading strategies, oversee technical infrastructures, and mentor a multidisciplinary team while managing performance analysis and risk.

The Geospatial Scientist leads data management initiatives, oversees data governance, and ensures quality and compliance of geospatial data for DHS.

The Workday Finance Analyst supports financial operations, configures Workday Financials, enhances workflows, manages projects, and provides user support to ensure efficient finance processes.

Own end-to-end data pipelines for multiple clients: extract, clean, transform, model and visualize data. Build dashboards and automated workflows, apply AI where useful, and present actionable insights to non-technical stakeholders.

Lead GTM productivity analytics: build and maintain predictive capacity models and dashboards, analyze GTM performance and attainment, support planning and forecasting, align metrics across Ops/Finance, and partner on data infrastructure improvements to drive GTM efficiency and growth.

Serve as a subject matter expert for data requests and system updates: design database structures, build and maintain data pipelines, write and optimize SQL, clean and transform data with SQL and Python, create dashboards and visualizations, analyze large datasets, and automate processes to improve data quality and workflows.

The Business Analyst will gather requirements from clients, support project lifecycles, conduct demos, and manage change, while utilizing ServiceNow. Responsibilities include documentation and relationship building.

As a Senior Analytics Engineer, you'll build data pipelines and dashboards, partner with stakeholders for KPI development, and ensure data quality across the company.

As a Business Intelligence Analyst, you will drive analytics, optimize processes, and generate insights to enhance customer success, developing dashboards and predictive models while collaborating with leadership and cross-functional teams.

Perform hands-on data cleansing, linking, validation, and standardization; monitor and report data quality metrics; identify recurring issues and recommend process or automation improvements; organize and streamline workflows; maintain documentation and data standards; test and provide feedback on tools and AI-assisted features; serve as a point of contact for data integrity questions.