Top Data Engineer Jobs in Los Angeles, CA

The Staff Data Engineer at Crunchyroll will lead the establishment of data engineering capabilities in AWS and GCP environments, implementing data infrastructure, mentoring a team, and ensuring reliable data delivery across departments. The role involves designing machine learning pipelines and improving data processing performance on a global level, while maintaining an empathetic and service-oriented team culture.

As a Data Science Associate Consultant, you will develop advanced algorithms, utilize machine learning and statistical techniques, and collaborate with clients to communicate analysis findings. You will also contribute to asset development, thought leadership, and mentor Associate team members.

As a Data Architect Manager at PwC, you'll lead the design and implementation of data architecture strategies and manage data integration and transformation solutions. You'll mentor team members, drive data strategy engagements, and ensure compliance with data governance policies.

As a Senior Machine Learning Engineer, you will develop and enhance machine learning platforms and infrastructure, collaborate with stakeholders, and automate pipelines for model deployment and monitoring, ensuring high quality and scalability of ML solutions.

The Senior Director of Data will lead a multidisciplinary team focused on data infrastructure, analytics, and machine learning. Responsibilities include overseeing analytics support, developing data strategies, and driving AI initiatives while ensuring data privacy, particularly in regulated industries like healthcare.

The role involves developing innovative pricing algorithms, handling complex datasets, communicating results effectively, and collaborating with cross-functional teams. The candidate is expected to research and develop machine learning models from concept to production, specializing in optimization and forecasting in a fast-paced environment.

The Senior Data Engineer will build and maintain data annotation and transformation pipelines, ensuring high data quality for machine learning projects. Responsibilities include establishing workflows for continuous data delivery, supporting data governance, and collaborating with ML teams to streamline data processes.