Top Senior Analysis Reporting Jobs in Greensboro, NC

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Develop customer analytics that improve retention, growth, adoption, and operational performance. Build and maintain BI dashboards, customer health metrics, churn-risk reporting, and predictive models. Partner with Client Success, Product, Sales, and Operations to translate complex findings into actionable recommendations. The role also owns data quality, governance, reporting automation, workflow improvements, and stakeholder communication.

Develop and support enterprise BI solutions, including dashboards, reports, governed datasets, data marts, and ETL/ELT processes. Translate business requirements into technical designs, perform data analysis and validation, troubleshoot production issues, and maintain documentation, governance, security, and data-quality standards. Lead-level candidates will guide technical decisions, mentor team members, and coordinate complex initiatives and incidents.

Leads advanced quantitative market research from client discovery through study design, analysis, interpretation, and recommendations. Establishes methodological and data-quality standards; designs segmentation, MaxDiff/conjoint, pricing, experimental, and longitudinal studies; evaluates sampling, weighting, statistics, and respondent quality. Leads senior client discussions, translates findings into business implications, partners with technical and execution teams, evaluates emerging research methods, and mentors researchers and analysts.

Own the analytics engineering roadmap, architecture, and production data models. Build and optimize dbt models using SQL, Python, and Databricks; establish dimensional models, governed metrics, testing, documentation, lineage, monitoring, and CI/CD practices. Partner with data engineering, analysts, BI developers, and stakeholders to deliver reliable analytics products, improve performance and cost efficiency, investigate data quality issues, and mentor team members.

Build and maintain reusable analytical data products, canonical entities, data marts, semantic layers, and governed metrics across business domains. Establish modeling, documentation, quality, lineage, and change-management standards; improve data reliability, self-service analytics, and production governance. Partner with Data Platform, Trust, Data Science, Applied AI, and business stakeholders on orchestration, observability, access controls, feature tables, model outputs, and shared metric definitions. Remain hands-on with SQL, Python, testing, code reviews, documentation, and technical coaching.

Conduct end-to-end analysis of complex healthcare data, including medical and pharmacy claims. Build and maintain SQL queries and analytical datasets, create visualizations, document methodologies, validate data, and translate findings into actionable business recommendations. Collaborate with analysts, data scientists, underwriters, product teams, account management, and cost-containment stakeholders to operationalize sustainable analytics solutions.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

Design and operate production-grade data pipelines, backend services, APIs, and agentic AI workflows. Build scalable systems that ingest, transform, and operationalize data for customer-facing and internal applications. Translate ambiguous operational workflows into reliable architectures, improve service observability and performance, and own technical decisions across data modeling, system design, security, and compliance.

Lead GTM analytics across performance measurement, channel economics, data governance, forecasting, planning, and executive reporting. Own metric definitions, data standards, analytical priorities, and business requirements while partnering with Marketing, Data Science, Finance, RevOps, and Engineering. Analyze funnel, attribution, CAC, payback, conversion, retention, pipeline, and territory performance, translating findings into investment recommendations and strategic decisions for senior leadership.