Top Hybrid Data Engineering Jobs

The Data Engineer III will design and implement AI-driven solutions, develop ontologies, lead projects, and mentor team members.

As a VP in QRMC, develop and maintain mathematical models, enhance risk analytics platforms, design algorithms, and work on product innovation in finance.

Lead the data architecture strategy and implementation, ensuring compliance with standards while collaborating across departments to optimize processes and manage initiatives.

Lead a team to develop and maintain risk models, oversee model analytics, monitor performance, and collaborate with cross-functional teams. Utilize mathematical models and coding in R and Python for financial products.

As a Staff Data Engineer, lead the technical roadmap for data infrastructure, manage complex data architectures, and mentor engineers while driving the organization's data platform innovations.

Develop and refine self-adaptive machine learning models for trading systems, collaborating with engineers to deploy solutions and improve performance. Embrace marketplace uncertainty to enhance decision-making processes.

As a Senior Data Engineer, you'll design and develop models to integrate diverse data sources for analytics and AI applications, ensuring data consistency and quality across domains.

The GTM AI Agent Builder will build AI foundations for Gusto's revenue engine, enhancing Sales, Marketing, and Partner teams' efficiency through automation and intelligent workflows.

Lead and mentor a team of Machine Learning engineers to design, deliver, and optimize ML systems that impact business revenue while collaborating with cross-functional teams.

The Analytics Engineer will support business stakeholders by creating data models, integrating new data sources, and collaborating with teams to improve analytics and reporting capabilities.

As a Trading Analytics Engineer, you'll develop tools for traders, improve workflows, and optimize data access using Python and SQL to support financial analysis.

The analyst will develop data pipelines, design automated reporting solutions, conduct data analysis, and maintain data quality to support business decision-making.