Top Data Engineer Jobs in Santa Cruz, CA

The Staff Machine Learning Engineer will work with product and marketing teams to personalize user experiences, build and deploy machine learning models, and improve growth and engagement through data-driven insights. They will utilize machine learning algorithms and techniques to drive personalization across Cash App and Afterpay.

The Bilingual Data Conversion Specialist is responsible for managing data consulting activities associated with client projects, ensuring accuracy in data conversions, and creating key performance indicators. The role involves collaboration across teams to monitor product delivery and enhance data quality standards, while also acting as a subject matter expert in analytics tools.

The Senior Data Engineer will build, optimize, and maintain data pipelines, define reusable data sets, design data validation frameworks, and ensure user privacy and security. This role requires a strong understanding of data processing and a modern data stack.

As a Senior Data Engineer at Allstate's Arity, you'll design and optimize data and machine learning workflows to enhance telematics solutions, collaborate with teams to support analytics and reporting, and ensure data quality while mentoring junior engineers.

The Senior Machine Learning Engineer will design and maintain ML infrastructure, develop and deploy machine learning models, monitor performance, collaborate with teams, and mentor junior engineers at Kikoff, a consumer fintech startup focused on credit-building solutions.

The Data Scientist III (ML Engineering) will develop and optimize machine learning solutions to improve Cedar’s healthcare platform. Responsibilities include deep data analysis, building pipelines, collaborating with engineering teams, and evaluating model performance to enhance patient experiences.

The Big Data DevOps Lead will enhance Big Data infrastructure efficiency, coordinate inter-team initiatives, and lead architecture efforts. Responsibilities include maintaining system reliability, deploying monitoring tools, and implementing disaster recovery practices. On-call support for production systems is also required.