Top Hybrid Data Engineering Jobs in San Francisco, CA

Lead architecture and delivery of SoFi's data platform: build scalable data pipelines, drive cloud-native and AI-enabled tooling, enforce governance and security, mentor senior engineers, and support production on-call.

The Lead Data Engineer will design and build the data warehouse, develop ETL/ELT pipelines, and establish data governance and quality practices while building a team to support data-driven growth.

The Data Engineer will build datasets and pipelines for marketing and sales reporting, collaborating with various teams to support data needs and ensure scalable solutions.

Develop and implement advanced algorithms, apply statistical techniques, collaborate with clients, assess technologies, and mentor team members.

Lead a team of data engineers and analysts, manage cross-functional data projects, and integrate insights to improve product and customer value.

Transform Electronic Health Records (EHR) data into structured assets, build scalable data pipelines, and apply Generative AI techniques for analytics within a healthcare-focused team.

The Staff Analytics Engineer develops data pipelines and models, supports business analytics needs, and collaborates with stakeholders to improve data reporting and integration for Corporate teams.

The Staff Analytics Engineer will develop data pipelines, structure datasets, and create reports to support corporate teams, ensuring data quality and effective stakeholder communication.

As a Senior Analytics Engineer, you will build and maintain data products, create ELT pipelines, and influence the analytical roadmap while mentoring engineers.

Lead design, build, and maintenance of scalable data pipelines and lakehouse infrastructure using PySpark, Databricks, and Airflow. Own data & ML platform (Terraform, Databricks), improve observability, data quality, and developer tooling, and partner closely with Data Science and engineering teams.

Design, build, and operate large-scale batch and streaming data pipelines and feature datasets for ads ML systems. Ensure correctness, reproducibility, SLA compliance, observability, and integration with online feature serving while driving architecture and best practices.