Data Engineer Jobs in Berkeley, CA

As the Founding Data Engineer, you'll unify data sources, build real-time pipelines, ensure data quality, serve ML & analytics, and optimize systems at scale.

The Senior Software Engineer will enhance Doximity's data platforms, collaborating with data teams to resolve challenges, and implement solutions to improve the data stack.

Responsible for leading the Data Platform initiatives including data ingestion, processing, and storage, while collaborating on system scalability and reliability.

Design, implement, and maintain scalable data infrastructure solutions using Snowflake. Collaborate with stakeholders for data insights and manage data pipelines.

Design, build, and maintain scalable data pipelines for machine learning; optimize performance of computing clusters; collaborate with research teams for model training.

The Data Engineer will enhance data infrastructure and drive data-driven decisions by developing data pipelines, collaborating with teams, and improving data architecture.

The Principal Data Engineer will design and maintain ETL pipelines, optimize data infrastructure, and collaborate with Data Scientists to support AI/ML model development.

As the first data hire, you'll build the data infrastructure, optimize SQL queries, automate workflows, and analyze metrics impacting product and growth initiatives.

The Data Engineer will design and support data infrastructure and products to meet internal and customer needs while promoting a robust data culture.

As a Data Engineer, you'll design, build, and optimize data models and pipelines, ensuring data quality and creating dashboards.

The Senior Data Engineer will architect low-latency systems, build sports betting data products, integrate datasets, develop APIs, and support production systems.

As a Python engineer on the data platform team, you'll enhance tools for data pipelining and analytics, collaborate with teams to address challenges, and improve software engineering culture at Doximity.

A Principal Software Engineer will enhance and deliver AI/ML technology products, collaborating across teams to improve engineering practices and leading AI/ML feature development.

Develop and optimize high-performance data ingestion and processing pipelines for LLM-powered financial applications, handling millions of documents.

As a Data Engineer, you design and optimize data pipelines, warehouses, and lakes, collaborating with analysts and engineers to support BI and ML initiatives.

As a Growth Data Engineer, you'll build and maintain ETL pipelines, ensure data reliability and governance, and collaborate with marketing and engineering teams to optimize data usage for customer growth.

The Analytical Data Engineer will build and optimize ETL data pipelines, contribute to data infrastructure, and collaborate with data scientists for analysis.

The Lead Data Engineer/Architect designs and implements GCP solutions, focusing on data pipelines, architecture, and leadership in a client-facing role.

As a Senior Data Engineer, you will develop and optimize data architectures, build user-centric data solutions, and ensure data accuracy and accessibility.

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.

The Senior Data Engineer will design, build, and maintain scalable data pipelines, optimizing data systems to support efficient analytics and operations.

As a Data Engineer at Figma, you'll build and maintain scalable data pipelines, optimize data models, and collaborate with teams to ensure timely data delivery.

The Data Engineer II will design, develop, and improve analytical data models and pipelines, collaborating across teams to meet data needs.

The Staff Software Engineer will curate data, build ETL workflows, automate processes, and serve as a liaison between scientific and engineering teams to enhance research data management.

Develop and maintain data pipelines for gathering post-training datasets, ensuring data quality and efficiency in processing and generation.