Top Hybrid Data Engineering Jobs

Design, build, and maintain scalable data pipelines integrating diverse sources into analytics platforms. Own projects, standardize pipeline tooling, implement automated data tests, ensure security/PII handling, support on-call troubleshooting, and mentor team members to enable reliable, production-grade data delivery.

Lead the development and implementation of automation techniques using AI and machine learning to improve life underwriting operations and processes.

The Lead Data Specialist will establish data architecture principles, develop solutions, and collaborate with teams to enhance ERP integrations and data management for the Cat Digital team.

Owner-operator for data processing automation: audit and redesign workflows, prototype scripting and low-code automations, configure client workflows, and translate messy private-markets data into clear technical requirements for the Data team.

Lead a team of data engineers and analysts, manage cross-functional data projects, and integrate insights to improve product and customer value.

Build data and AI-powered internal tools (meeting prep automation, portfolio benchmarking, diligence tooling) used by M13's investment team; produce clean, maintainable code, documentation, and collaborate with end users to iterate on production-ready systems.

As a Staff Data Engineer, you will lead data engineering initiatives, architect and build scalable data pipelines, and mentor teams, enhancing Crunchyroll's data infrastructure within AWS and GCP environments.

Transform Electronic Health Records (EHR) data into structured assets, build scalable data pipelines, and apply Generative AI techniques for analytics within a healthcare-focused team.

Lead the development of scalable data platforms and analytics systems, overseeing teams in building data pipelines, advanced algorithms, and dashboards for data-driven decision making.

Lead the engineering and operational excellence of data collaboration ecosystems, focusing on scalable data architectures, automation, and MLOps practices.