Top Analysis Reporting Jobs in Greensboro, NC

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Builds and maintains scalable data pipelines and cloud infrastructure supporting an AI biomedical research platform. Responsibilities include ingesting, harmonizing, validating, and documenting multimodal neurodegeneration datasets; managing cloud storage, compute, permissions, and costs; implementing de-identification and data governance workflows; supporting research data repositories and AI/ML teams; and maintaining data lineage, catalogs, and technical documentation. The role requires strong Python and SQL skills, software engineering discipline, cloud experience, and neurodegeneration research data expertise.

Design and architect robust ETL/data pipelines on cloud platforms. Build and automate data ingestion and preprocessing using Spark/Databricks, AWS Glue, Airflow. Implement warehousing architectures (EDW, DM, ODS, MOLAP/ROLAP), ensure SQL-based data quality, use version control, and deliver tested production pipelines while communicating with stakeholders.

Build and maintain product analytics dashboards, validate and QA event tracking, translate event data into analytics-ready datasets, run funnels/cohorts/retention analyses, support A/B testing, and surface actionable product insights to Product, Growth, and Data stakeholders.

Design, build, and maintain data pipelines, dashboards, and predictive models. Analyze large datasets to generate actionable insights, collaborate with stakeholders, ensure data quality and governance, and document/share analytics best practices to support Marketing, Product, Operations, and Workforce teams.

The Data and Analytics Consultant will design, develop, and implement data reporting, visualizations, and analytics solutions for clients while assisting them in data understanding and value extraction.

Design, build, and maintain scalable ETL/ELT data pipelines and data platform architecture. Build programmatic pipelines (primarily Python), optimize analytical data models (SQL), integrate sources into Snowflake, use orchestration/transformation tools (dbt, Airflow, Dagster), improve data quality/observability, and partner with product, engineering, and analytics to deliver reliable data for decision-making.

Designs and operates scalable serverless AWS data pipelines, data models, ETL/ELT workflows, feature stores, and analytics-ready data assets. Partners with AI, Analytics, Product, and Underwriting teams to support reproducible ML training and scoring, MLOps, data quality, monitoring, governance, and production troubleshooting. The role handles sensitive healthcare and financial data, optimizes serverless workloads, supports batch and near-real-time processing, and applies infrastructure-as-code, CI/CD, documentation, and responsible data-use practices.

Design and maintain healthcare data integration pipelines, models, and validation processes for claims, billing, clinical, pharmacy, EMR, FHIR, and value-based care data. Collaborate with clients and internal analytics, product, and customer success teams to interpret data, document business rules, support implementations, and build reusable Ursa data models. Apply healthcare domain expertise, data security practices, HIPAA compliance, and appropriate judgment when using AI-assisted tools.

Lead design of end-to-end analytics and data solutions, produce architecture diagrams and technical designs, guide delivery teams, run client discovery and requirements sessions, support pre-sales and proposals, build reusable frameworks, and manage and mentor a delivery team to ensure quality and alignment to architecture.