Top Remote Data & Analytics Jobs in Greensboro, NC

Build and optimize end-to-end data pipelines, production-grade dbt models, BigQuery schemas, Fivetran integrations, and Looker semantic layers. Partner with clinical, operations, finance, and growth teams to translate business questions into analytics projects focused on acquisition, attribution, conversion, and funnel performance. Establish data quality, governance, documentation, testing, and version-control practices while mentoring analysts and supporting secure, compliant data use.

Build and operate cloud data capabilities, including ingestion pipelines, transformations, data models, curated data products, and data-quality controls. Use Snowflake, dbt, SQL, and Python to deliver governed data for analytics, applications, automation, and AI workflows. Implement testing, CI/CD, documentation, lineage, access controls, monitoring, incident resolution, and performance optimization. Collaborate with engineering, analytics, governance, security, and business teams in an Agile, product-oriented environment with active AI-assisted development.

Own the analytical function for a core business area, defining business questions, metrics, reports, analyses, experiments, and recommendations. Partner with product, growth, finance, and operations leaders to drive decisions and measurable outcomes. The role requires solving ambiguous problems, independently structuring and interpreting data, communicating insights persuasively, and executing initiatives end to end.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems and metrics, build reports, analyze data, design experiments, generate insights, influence leadership decisions, and drive initiatives through execution. The role requires strong first-principles thinking, independent data skills, persuasive communication, and ownership of ambiguous, high-impact problems.

Lead the technical direction, architecture, and roadmap for the company’s data platform. Design reliable large-scale pipelines, establish standards for modeling, testing, observability, and orchestration, and mentor data engineers. Partner with engineering, product, analytics, and business stakeholders to align infrastructure investments. Lead incident root-cause analysis, evaluate data technologies, and support scalable analytics, machine learning, and real-time processing capabilities.

Design and operate scalable, cloud-native data pipelines and orchestration systems for imaging, biomarker, and healthcare data. Build Databricks and distributed processing workflows, integrate DICOM data into data lakes, and implement validation, observability, monitoring, and fault tolerance. Collaborate with data science, AI, R&D, product, and data management teams to deliver secure, compliant, reliable data for analytics and machine learning.

Design and build petabyte-scale multimodal data pipelines and infrastructure (text, audio, images, video). Own and maintain systems like Spark, Ray, and vector databases, partner with pre/post-training teams to improve data recipes through experimentation, and support large-scale dataset storage and processing for AI model training.

Own and maintain the companys data infrastructure and pipelines. Design, build, and monitor ETL/ELT workflows and data architecture; integrate SaaS sources; enforce data quality, standards, and governance; partner with stakeholders to deliver analytics-ready data and meet data availability SLAs.

Analyzes, validates, and prepares distribution data; develops dashboards, reports, and visualizations; supports warehouse management systems, labor management, configuration, testing, and continuous improvement. Partners with Distribution, IT, process teams, engineers, and vendors to provide business intelligence, troubleshoot systems, communicate data insights, and support operational and strategic decisions.

Designs and operates an AWS-native lakehouse supporting analytics, reporting, visualization, and AI/ML. Builds batch and streaming ingestion pipelines using Python and PySpark, Apache Iceberg data structures, governance and metadata services, quality controls, security, observability, and CI/CD automation. Optimizes performance, scalability, reliability, and cost while collaborating with cross-functional technical teams and documenting architecture, operations, and secure configurations.