Top Analysis Reporting Jobs in Charleston, SC

Embedded with a single enterprise client to engineer and optimize large-scale data ingestion, design scalable analytical and operational data models, build/maintain cloud data warehouses, improve internal tooling and workflows, and act as a technical partner guiding strategic data decisions.

Build and maintain executive and project-level dashboards, standardize and cleanse source data, define reporting conventions and KPIs, perform data quality checks, coordinate with stakeholders, and apply AI/LLM tools to improve reporting workflows and efficiency.

Lead architecture and scaling of the Databricks data warehouse and self-service data platform. Build reusable batch and streaming frameworks, enforce governance, SLOs, lineage, observability, CI/CD, and multi-tenancy. Mentor engineers, run design reviews, and ship documented patterns to enable analytics, data science, and app teams to move quickly without blocking on platform tickets.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

Manage end-to-end analytics projects, lead cross-functional teams, ensure client satisfaction, and utilize AI tools to enhance data insights.

Lead end-to-end customer engagements to architect, implement, and optimize large-scale Spark/Databricks workloads. Troubleshoot and tune PySpark and DBSQL jobs, design PoCs and workshops, advocate strategically for customers, collaborate cross-functionally, and mentor junior engineers on Lakehouse best practices.

Senior individual contributor responsible for designing and aligning data solutions for commercial profitability and enterprise platforms. Translate business outcomes into scalable technical designs, guide development teams on best practices, ensure alignment with enterprise architecture and governance, standardize data models, and collaborate with product, finance, and technology stakeholders to deliver accurate, performant, and reusable data assets.

Analyze customer and caregiver data to identify workflow improvements, build dashboards and visualizations, support Customer Success with QBRs and presentations, translate findings into actionable recommendations, and create scalable data processes while advising Product and Engineering.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Build and maintain production-grade data pipelines and ELT processes using Python, SQL, Airflow, and dbt within AWS. Design optimized schemas in PostgreSQL and Redshift, leverage AI/LLMs to improve development, automation, testing, and data quality, and scale infrastructure for large healthcare datasets.