Top Data & Analytics Jobs in Greensboro, NC

Executes eDiscovery analytics and AI-assisted workflows in Relativity and related platforms, including TAR, continuous active learning, generative AI classifiers, validation sampling, statistics, and structured analytics. Maintains accurate matter records, tracking, time entries, audit trails, and quality controls. Provides weekend coverage, monitors request queues, supports deadline-driven projects, documents handoffs, and escalates sensitive issues. The role includes structured training and progression toward an eDiscovery consultant track.

Analyze business data using SQL, BigQuery, Tableau, Python, and related tools to identify trends, build dashboards, maintain KPIs, and deliver actionable insights. Translate ambiguous business questions into reporting, automation, AI-powered workflows, and technical solutions. Partner with technical and business stakeholders, improve data quality and storytelling, and explore emerging AI technologies such as agentic workflows and LLM-powered automation.

Lead and grow data engineering and analytics engineering teams while overseeing data platform modernization, BigQuery and Looker migrations, reusable dbt models, self-service capabilities, governance, reliability, and data foundations for AI and ML. Partner with technical and business stakeholders to prioritize roadmaps, manage hiring and performance, improve adoption, and protect sensitive healthcare data.

Own executive, board, investor, financial, and operational reporting for leadership. Build scalable dashboards, define consistent KPIs, reconcile data across systems, investigate performance changes, and translate complex business questions into actionable insights. Partner with Strategy, Finance, and senior leaders to automate recurring reporting, improve data accuracy, and support actuals-versus-budget-versus-forecast analysis.

Owns analytics for BILL’s payments products, setting the measurement agenda, defining KPIs and OKRs, building production-grade dbt models, and conducting experimentation and causal inference. Partners with Product, Engineering, Finance, Marketing, and leadership to identify opportunities, improve data quality, automate reporting, and influence roadmap decisions. Presents executive insights and defends metric definitions and recommendations.

Leads the architecture and execution of migrating clinical, dental, pharmacy, and administrative data from more than ten legacy systems into MHS GENESIS. Responsibilities include source analysis, canonical data modeling, source-to-target mapping, ETL architecture, reconciliation, data-quality frameworks, cutover sequencing, tooling standards, team leadership, and production of migration artifacts for government review.

Processes client-requested data changes, creates data transfer packages, and writes SQL queries against Microsoft SQL databases. Conducts risk assessments, verifies data updates and transfer datasets, creates stored procedures, monitors data across databases, and supports clinical study implementation. Requires strong attention to detail, documentation, communication, prioritization, and occasional after-hours technical support.

Serves as a technical liaison between clients, business teams, and IT. Analyzes business and system needs, translates requirements into functional specifications, develops SQL queries, researches issues, documents workflows and business rules, and supports system enhancements, testing, implementation, training, and troubleshooting. Maintains client system documentation and project plans while ensuring timely, accurate service delivery and continuous improvement.

Contributes to building and optimizing a large-scale data lake and reliable data pipelines for cybersecurity analytics. Responsibilities include data modeling, distributed processing, workload optimization, monitoring, observability, CI/CD, code reviews, and supporting MLOps. The role collaborates with senior engineers to improve processes and deliver cost-effective cloud data solutions.

Build customer-facing data products for the single-family rental market. Responsibilities include architecting real estate data models, designing reliable data pipelines and workflows, shaping data platform architecture, establishing engineering standards, and developing real estate domain expertise. The role partners with data science and product teams and requires leadership in scalable data engineering.