Top Remote Data & Analytics Jobs in Hampton, VA

Lead HR Service & Digital Experience analytics by owning reporting, ensuring data integrity, and translating operational and contractual HR data into actionable insights for senior leaders to improve caregiver and leader experiences.

The Manufacturing Analytics Engineer will manage the analytics pipeline, transforming operational data from OT systems into actionable insights for decision-making, while collaborating with Engineering, IT, and Operations.

Design and build scalable data solutions and robust pipelines on Palantir Foundry to enable analytics and AI/ML. Model enterprise datasets, develop Foundry applications and custom frontends, integrate cloud data services (AWS, Snowflake), apply DevOps practices (CI/CD, containers), and optimize distributed systems for performance. Insurance domain experience is a plus.

Perform end-to-end data analysis and transformation for large-scale P&C insurance datasets. Develop source-to-target mappings, validate and profile data, optimize complex SQL queries, and collaborate with business SMEs and technical teams to ensure data quality, standardization, and accurate technical documentation.

Design and implement AWS-based data architectures and lakehouse solutions, lead data engineering teams, build ETL and real-time pipelines (Kafka/Kinesis), use DBT and Airbyte for ingestion and transformation, orchestrate workflows with Airflow/MWAA, enforce security/compliance, and guide infra automation with Terraform/CloudFormation and CI/CD.

Lead design and implementation of BI solutions, maintain Power BI relational models, build enterprise dashboards and self-service analytics, translate business requirements into technical specifications, lead cross-functional analytics projects, apply AI-enhanced analytics, and promote data governance and data literacy across Finance, HR, and conservation programs.

Lead enterprise analytics strategy and execution, build scalable self-service BI and dashboards, manage complex analytics projects, partner with cross-functional stakeholders, perform advanced analyses as subject-matter expert, and drive AI-enhanced analytics adoption to inform strategic decision-making.

Analyze marketing channel performance (Paid Media, CRM, Email, engagement), prepare recurring reports, consolidate data into actionable summaries, perform QA on reports/datasets, support client recommendations and optimizations, collaborate with cross-functional teams, and use AI tools responsibly to improve efficiency.

Analyze customer and caregiver data to identify workflow improvements, build dashboards and visualizations, support Customer Success with QBRs and presentations, translate findings into actionable recommendations, and create scalable data processes while advising Product and Engineering.

Lead reporting, analysis, automation, and QA for revenue cycle analytics. Build Power BI and Excel reports, design data models, automate workflows with SQL/Python, integrate AI agents, validate data integrity, and deliver actionable insights to leadership and operations.

As an Interface Specialist II, you will develop and optimize automated processes, support integrations, manage projects, and collaborate on process improvements.

Lead end-to-end migration of VA legacy contract and acquisition records into Appian, overseeing extraction, ETL design, mapping, validation, cutover planning, provenance preservation, and post-migration monitoring. Coordinate with SMEs, architects, and Financial Systems leads, run migration rehearsals, and produce documentation and knowledge transfer for O&M sustainment.

Design, develop, and deliver analytics and BI products on Palantir Foundry integrating Appian and VA financial systems. Build dashboards, data models, and data quality monitoring; translate stakeholder requirements into KPIs and visualizations; support pipeline design, documentation, and training for VA acquisition and financial analytics.

Design, build, and optimize end-to-end ETL/ELT pipelines using Python and PySpark for large structured and unstructured datasets. Ensure data quality through validation and reconciliation, troubleshoot performance issues, implement CI/CD and code review practices, and collaborate with IT and business stakeholders to translate requirements into scalable data solutions.

Lead global data governance strategy and operating model, establish enterprise data management/MDM and data certification practices, build and lead the global governance function and steward program, drive change management and adoption across business lines, partner with cross-functional teams to resolve data risks and embed governance into workflows, and deliver measurable outcomes against the governance roadmap.