Top Remote Data & Analytics Jobs in Raleigh, NC

Lead design and governance of a large-scale AdTech data layer: model conceptual/logical/physical schemas, profile and map data flows, define batch and real-time ingestion and integration patterns, architect cloud-native AWS solutions (including graph/vector/RAG use cases), establish governance, and produce architecture artifacts and implementation roadmaps while collaborating with engineering, product, and cloud teams.

Lead a data engineering team building and operating NPR's data warehouse, pipelines, BI, and personalization systems. Responsibilities include people leadership, hiring, cross-functional collaboration, Agile delivery, technical design and code review, automation for developer experience, incident response, and vendor management.

Develop and lead healthcare analytics and BI solutions for HIS products: design, code, and deliver enterprise-scale applications and dashboards, work with AWS data services and BI tools, support product development and troubleshoot reporting issues for global customers.

The Lead Risk Analytics Data Scientist will develop risk pricing, design data models, automate workflows, and communicate insights to leadership. Responsibilities include owning the risk narrative, building analytics foundations, and utilizing AI to enhance efficiency.

The Senior BI Analyst will gather requirements, develop scalable reporting solutions, and create dashboards using Power BI, ensuring data-driven decisions across the organization.

Lead Data Engineer responsible for defining architecture, engineering standards, and best practices for the enterprise data platform, collaborating with distributed teams for modernization and scalability.

Assist in collecting, cleansing, and analyzing datasets to produce actionable insights, reports, and dashboards for informed business decisions.

Lead architecture, operation, and continuous improvement of cloud-native, enterprise-scale data platforms (Snowflake, cloud storage, streaming). Translate requirements into roadmaps, design and optimize data warehouses and lakes, automate operations with Python and IaC, mentor engineers, and deliver managed services across multiple client accounts while driving reliability, security, and performance.

Lead operation, reliability, and continuous improvement of cloud data platforms (streaming, lakes, warehouses). Provide architecture and hands-on operational support, collaborate cross-functionally, drive automation, mentor engineers, and act as trusted advisor for enterprise clients.

Lead design, implementation, and optimization of cloud-native data platforms (Snowflake, AWS, Azure) for managed-services clients. Translate business requirements into secure, scalable architectures, drive migrations and performance tuning, and provide hands-on technical leadership. Collaborate with cross-functional teams, mentor junior engineers, and contribute to best-practice frameworks, automation, and pre-sales efforts.

The Data Scientist designs and builds data structures for AI applications, manages vector and feature stores, leads data discovery, and collaborates on responsible data use while ensuring high-quality, governed datasets for ML models and applications.

The Principal Data Architect will design and oversee the enterprise data architecture, ensuring effective modeling, integration, governance, and consumption of data across the organization. Responsibilities include building data models, implementing lakehouse strategies, and partnering with multiple teams for the evolution of data practices.

Design, build, and maintain scalable, reliable data systems and pipelines. Partner with Product, Sales Ops, and Finance to deliver analytics and self-service tooling. Maintain SLOs, improve pipeline reliability, implement testing and documentation, and contribute to data platform strategy and operations.

Customer-facing specialist guiding enterprise cloud data engineering and lakehouse transformations. Architect and optimize large-scale data pipelines, perform performance and load testing, support pre-sales POCs and workload sizing, lead adoption workshops, and mentor peers to drive Databricks platform adoption.