Top Hybrid Data Engineering Jobs

Build and maintain data quality scripts and automated validation workflows, support large data migrations to modern platforms including public cloud, triage and repair data quality issues, prepare datasets for client-facing materials, and ensure access to databases for business intelligence tools.

Assist in developing, testing, and maintaining software applications, collaborate on designs, participate in code reviews, and contribute to documenting processes.

Design, build, and maintain data pipelines and tooling; collaborate with cross-functional stakeholders to deliver governed, high-quality datasets; own data quality and drive adoption of produced datasets.

The Senior Analytics Engineer will build and scale the analytics foundation, transforming data for decision-making across multiple teams and ensuring data quality and automation.

Build and maintain scalable ETL pipelines and storage for multimodal clinical and NLP data; ensure data quality, security, and compliance; implement synthetic-data generation and real-time model evaluation infrastructure; create dashboards and documentation to support AI research and monitoring.

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.

Design, develop, test, implement, and support cloud-based data solutions. Collaborate across Agile teams on distributed, streaming, and full-stack systems, mentor engineers, and perform unit tests and code reviews to deliver performant data products.

Design, develop, test, and support cloud-based data solutions and streaming systems. Collaborate with Agile teams, mentor engineers, and ensure performant, well-tested code for large-scale financial data workloads.

Lead and mentor engineering teams to design, develop, test, and deploy cloud-based data solutions. Build distributed, real-time and batch data pipelines using big data technologies, databases, and cloud warehouses. Collaborate with product managers and peers to deliver performant, production-ready systems and ensure code quality through testing and reviews.

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 delivering enterprise data architecture and solutions for the Cat Helios platform. Define strategies, processes, and roadmaps; partner with product and business leaders to create scalable, reusable data products; ensure data sharing, governance, and high-quality architecture deliverables.

The Data Engineer will architect and deploy Big Data environments, manage data pipelines, and ensure data integrity across the organization.