Top Senior Data & Analytics Jobs in Greensboro, NC

Design, develop, and maintain Microsoft Power Platform applications and dashboards (Power Apps, Power Automate, SharePoint, Dataverse, Power BI). Translate stakeholder requirements into technical solutions, create data visualizations and recurring reports, perform trend analysis, troubleshoot integrated solutions, and support system design, configuration, and continuous improvement to improve operational performance and remediation tracking.

Lead and execute quality control and data validation for operational dashboards and reporting. Develop QC procedures, track corrective actions, perform root cause analysis, validate dashboard accuracy, document findings, and coordinate with technical teams to resolve data discrepancies and improve processes.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

Lead a team delivering actuarial statistical reporting, regulatory submissions, and analytics. Ensure compliance with bureau requirements, drive automated reporting and UAT for policy/claims systems (Guidewire, Solartis), partner with actuarial, technology, and vendors, and manage stakeholder/regulator interactions while mentoring staff and improving data governance and reporting processes.

Lead a team delivering actuarial, regulatory, and operational statistical reports and analytics. Ensure compliance with bureau submissions, drive reporting automation, define data requirements, lead UAT for policy/claims systems, and manage vendor and regulator interactions to support pricing, reserving, underwriting, claims, and financial analytics.

Lead enterprise data architecture, migrations, and data services; define standards and roadmaps; oversee complex ETL and third-party integrations; deliver analytics, BI dashboards, and commercialization of AI analytics offerings; build the Data Strategy & Services organization and ensure continuity for legacy clients while partnering across Product, Engineering, Sales, and Customer Success.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.

Lead technical design and implementation of cloud data pipelines and data warehouse models using PySpark, Snowflake, and AWS. Drive cloud migration, establish engineering standards, optimize performance, coordinate onshore/offshore teams, engage stakeholders, mentor junior engineers, and ensure delivery of insurance analytics solutions.

Build and maintain P&C actuarial pricing models and indication reports; refine pricing methodologies; analyze large datasets; automate workflows with Python, SQL, and Excel; create dashboards and presentations; collaborate with underwriters, claims, program teams, and reinsurers; and drive process and methodology improvements.

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.

Lead post-sales and revenue analytics by integrating product telemetry, support signals, and revenue data. Build scalable data models, reusable datasets, and semantic layers. Define SaaS metrics, forecast revenue and usage, predict expansion and churn, and deliver dashboards and insights to drive renewals, expansion, and revenue growth.

Design, build, and maintain scalable cloud-based data pipelines and lakehouse platforms. Integrate diverse business systems, ensure data quality, support analytics and AI, implement governance, and mentor junior engineers while optimizing performance and enabling enterprise reporting and ML use cases.