Top Hybrid Data & Analytics Jobs

The Senior Data Engineer will build and optimize data pipelines, ensure data integrity, and collaborate with stakeholders to meet business requirements while implementing best coding practices.

Work with client and internal teams to design and implement enterprise data solutions. Lead workstreams, translate business problems into technical designs, support delivery activities (architecture, prototypes, test plans, deployment), mentor junior staff, and apply best practices across cloud, data platforms, and visualization tools.

As a Senior Data Scientist in Product Analytics, you will analyze data to guide product decisions, support product managers, and improve financial stability for users.

The AI Financial Planning Analyst reviews and improves financial guidance from AI systems, works across teams to enhance member experiences, and ensures compliance with regulations.

Lead design and implementation of scalable, HIPAA-compliant data pipelines and platforms for healthcare ML. Build ETL, orchestration, and tooling for processing EHR, claims, pharmacy, and bioinformatics data; collaborate with data scientists to produce modeling-ready datasets and ensure data quality, reliability, and operational excellence.

The role involves analyzing customer engagement across channels, optimizing lifecycle marketing programs, and collaborating with various teams to improve marketing strategies.

Provide functional support for JD Edwards EnterpriseOne applications, focusing on inventory and distribution while enhancing workflows and user training.

The Business Insights and Planning Manager drives growth through strategic analytics, annual sales planning, and revenue intelligence, while ensuring global alignment and effective communication with stakeholders.

Analyze customer and merchandising data, build data pipelines and reports, write clean queries and code, conduct statistical analyses to identify trends and opportunities, and validate key metrics to inform business decisions.

Lead development of Navan's internal economic models, build probabilistic forecasts, run causal inference experiments (A/B, quasi-experimental), create pricing and retention frameworks, deploy production-quality Python models into AWS/Snowflake, and advise executives on economic strategy and risk.