Data Engineer Jobs in San Jose, CA

As a Software Engineer, you'll design and develop tooling for Gemini models, collaborate with researchers, drive developer experience, and mentor team members.

Lead the design and implementation of complex data systems, ensuring data quality and mentoring junior engineers while optimizing processes.

As a Data Science Engineer at Adobe, you will analyze data, develop machine learning models, and provide insights to drive business decisions in the B2B segment. Responsibilities include enhancing M/L models, implementing dashboards, and discovering opportunities for growth.

The BI Developer/Data Warehouse Architect will design and maintain business intelligence solutions, create self-service dashboards, manage ETL processes, and support data integration between systems.

As a Principal Data Processing Engineer, you will enhance a data processing engine, collaborate on open-source platforms, and optimize performance.

The Lead Data Engineer will develop and manage B2B data pipelines and infrastructure, ensuring data quality and collaboration with cross-functional teams for marketing analytics.

The Analytics Engineer will enhance the data foundation at Develop Health, improve analytics, and design metrics to foster decision-making across teams.

Design and enhance large-scale data pipelines for machine learning teams, ensuring high-quality dataset delivery and facilitating new model deployments.

Design and develop scalable data processing systems and pipelines, create storage solutions, and ensure data integrity through testing and monitoring.

Design and develop scalable data pipelines and storage systems for machine learning evaluation, and create tools for data annotation and monitoring.

As a Data Platform Engineer, you'll design and maintain scalable data systems, partner with teams on product direction, and ensure system reliability and security.

Develop high-performance components using Golang for the Litmus Data Platform, focusing on streaming data integration and system resilience.

Lead architectural design for Coupang's data ecosystem, drive technical decisions, and collaborate with engineering teams on implementations to enhance data capabilities and operational excellence.

Design and build software for data management, collaborating with NVIDIA research and internal teams to enhance cloud services and performance.

As a Senior Data Engineer, you will lead projects, design and maintain scalable data architectures using cloud technologies, and collaborate with stakeholders to enhance data solutions and business intelligence capabilities.

Design and develop ML/DL algorithms and architectures for automated data analysis, build scalable ETL pipelines, and work collaboratively on projects.

Transform complex data from network infrastructure into actionable insights by developing data extraction processes, automating via scripting, and utilizing visualization tools.

As a Senior Data Engineer at TRI, you will design and build robust data infrastructure and pipelines to support autonomy research and simulation workflows.

Develop backend systems to monitor and govern data use, ensuring compliance and building tools to enforce data policies across the organization.

The Principal Product Manager will lead the strategy for data platforms, enhancing data accessibility and usability while coordinating with engineering teams to optimize operations and improve scalability for Adobe products.

Join the Next-gen Firewall dataplane team to design and develop advanced networking software for next-generation firewalls, ensuring security and performance across multiple environments.

The Big Data Developer (Spark) will be responsible for Spark and Scala development, Hadoop performance tuning, and SQL coding. Requires extensive experience in data processing and performance optimization.

The Big Data Engineer will work with Java, Spark, and Hadoop to design and implement large-scale data processing systems, focusing on distributed systems and machine learning algorithms.

Design and operate distributed data systems for large-scale data ingestion, processing, and transformation. Collaborate with ML researchers for data preparation and ensure data quality and scalability.

Design and optimize large-scale data systems for risk management. Collaborate with teams to transform business needs into data solutions. Ensure data pipeline efficiency.