Top Analytics Jobs in Raleigh, NC

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 and implement end-to-end Microsoft Fabric data solutions (ingestion, Lakehouse, warehouse, real-time). Create architectures, reusable frameworks, and governance patterns for insurance datasets. Lead ingestion pipelines, optimize performance/cost, integrate with Azure services and security, support regulatory compliance, and provide technical direction to engineering and analytics teams.

Lead design and delivery of Snowflake-centric cloud data platforms. Engage with clients to gather requirements, architect scalable secure solutions, provide technical leadership, develop roadmaps, advise executives, and guide implementation best practices across the analytics stack.

Own the strategy, governance, architecture, modeling, and reporting foundation for People Experience data. Build governed metrics, semantic and consolidated data layers, dashboards, data catalogs, quality monitoring, and operating processes from the ground up. Manage intake, prioritization, access, privacy, enablement, and self-service adoption while partnering with stakeholders on analytics and future AI capabilities. The role combines strategic program leadership with hands-on SQL, data modeling, reporting, and team mentoring.

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.

The Senior Manager of Risk Analytics will lead credit risk strategies, manage portfolio performance, optimize decisioning frameworks, and drive business growth through analytics and predictive modeling.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Lead development of scalable marketing data models, pipelines, and BI solutions. Partner with Marketing to define metrics, build dashboards, enable self-service analytics, and drive data governance and best practices.

Design and maintain scalable dbt data models and Looker dashboards; partner with Finance, Product, and GTM to translate requirements into analytics solutions; ensure data quality with Data Engineering; perform analyses, forecasting, and QA to deliver trusted reporting and drive analytics initiatives.

Lead and grow a client-facing analytics team to transform measurement from reporting to strategic advisory. Define measurement strategy, develop scalable frameworks, advise executives, drive vertical analytics, partner with Product and Data teams, and embed AI to improve insight generation and operational efficiency.

Design, build, and operate the credit fund's end-to-end data and automation stack. Implement deal workflow, ingest borrower financials and payment telemetry, develop Python credit scoring and forecasting models, ship audit-grade investor and borrower dashboards, enforce security and auditability, and move into portfolio monitoring and underwriting while presenting results to LPs and borrowers.

Lead architecture and delivery of enterprise data transformation on the Guidewire Data Platform. Drive design, ingestion/egress, migration performance, and AI-enabled tooling. Facilitate inception and design review sessions, mentor teams, coordinate with partners, and ensure successful data platform adoption and repeatable delivery practices while spending significant time on customer sites.

Lead and own the population health analytics pipeline: intake and reconciliation of claims, eligibility, and MAO-004 data; identify and reconcile care gaps and closures; deliver practice-level performance analysis and billing reconciliations; build funnel instrumentation; document, standardize, and automate pipelines with Product and Engineering. Ensure billing-grade accuracy and support program measurement aligned to CMS reconciliation windows.

Lead analytics for law firm operations by writing advanced SQL and Python/R scripts, building executive dashboards, standardizing KPIs, analyzing case lifecycle metrics, identifying bottlenecks, and delivering actionable recommendations to drive capacity planning and process improvements.

The Data & Analytics Architect will design and implement analytics solutions, support client engagements, and lead data architecture projects, requiring extensive technical and consulting skills.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead analytics strategy and delivery for healthcare clients: gather reporting requirements, design and build enterprise data platforms (Databricks/lakehouse), develop and validate reports (including Epic reporting), influence decentralized data teams, and provide post-implementation support and modernization.

Lead analytics and data-product initiatives for healthcare clients: gather reporting requirements, design and develop reports/products, test and validate, provide post-implementation support, and partner with stakeholders to drive adoption and measurable value.

Lead analytics and insights for the CFO and exec team: produce executive-ready analyses, design scalable analytics infrastructure, leverage AI to accelerate workflows, partner cross-functionally to drive strategy and execution, and manage multiple high-impact initiatives.

Lead design and governance of enterprise ontologies, knowledge graph architecture, ingestion pipelines, and semantic layers. Enable semantic interoperability across data products, implement inference and reasoning, and establish standards, versioning, and data contracts to support scalable graph-driven analytics and AI augmentation.

Lead design and implementation of data models, write optimized SQL at scale, enable semantic layers and AI tooling, manage analytics infrastructure, build frontend data surfaces, and partner with product and engineering to structure source systems for reporting.

Build and maintain production dbt models on Databricks as the organizations source of truth. Ensure data quality via testing, CI/CD, and documentation. Create Databricks dashboards and semantic metrics, support external reporting, and partner with engineering and analytics teams to turn business problems into durable data products. Drive AI-native workflows using tools like Cursor and Claude Code.

The RevOps Analytics Engineer is responsible for maintaining data infrastructure, building reliable data systems, and supporting revenue analytics through effective data modeling and ETL/ELT processes.

Lead the modeling, forecasting, and analytics for go-to-market strategy. Collaborate with Sales and Finance on strategic revenue forecasts and actionable insights.

Own metrics and datasets that measure cloud, storage, streaming, and ML infrastructure efficiency. Build end-to-end analytics: metric frameworks, pipelines, dashboards, and narratives that inform executive and engineering investment decisions. Pioneer methods to quantify AI and agent-driven productivity, identify efficiency opportunities, and partner with data engineers and data scientists to deliver trusted, reusable insights and recommendations.