Top Lead Data Engineer Jobs in San Francisco, CA

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

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

Lead the development of healthcare applications and data systems, focusing on audience intelligence and compliance. Design scalable data pipelines, APIs, and reporting systems while ensuring high data quality and security in regulated environments.

The Lead Data Engineer drives enterprise-scale data solutions, oversees project scoping, and leads a team, ensuring best practices in data engineering.

The Lead Data Engineer will design and architect data retrieval systems, build inference infrastructure, improve data quality, and mentor a specialized team to enhance customer success through AI integration.

The Lead Data Engineer will architect and develop a healthcare data platform on GCP, guiding teams in pipeline development, data modeling, and ensuring compliance with HIPAA. They will collaborate across functions, set technical directions, and mentor junior engineers.

As a Lead Data Engineer, you'll optimize data workflows, manage data engineering projects, mentor junior engineers, and ensure robust data solutions meet business needs.

As a Lead Security Engineer, you will architect and manage Circle's security data platform, ensuring robust data ingestion, normalization, and response strategies while collaborating on security operations initiatives.

The Lead Data Engineer will design and build scalable data pipelines and infrastructure for trading analytics, overseeing data architecture and collaborating with cross-functional teams.

As a Lead Data Engineer, you will design and maintain data platforms, oversee data ingestion pipelines, enforce standards, collaborate on solutions, and ensure best practices in healthcare data operations.

The Scientific Data Engineer will design data architecture, build ETL/ELT pipelines, implement data quality checks, and develop semantic layers for AI-consumable data products in healthcare research.

The Lead Agentic Data Systems Engineer will design and maintain autonomous agents for data management and problem resolution, ensuring product delivery and governance with a focus on AI-driven workflows.

The Lead Data Engineer will manage the AI Data Platform, scale data pipelines, ensure data quality, and lead technical strategy for data initiatives.

The Lead Data Engineer will design and build scalable data pipelines on Databricks, develop ETL/ELT pipelines with PySpark, and mentor engineering teams for data transformation and analytics.

The Technical Lead will build software systems for AI-native drug discovery, design data pipelines, develop APIs, and collaborate with scientists to create efficient workflows and solutions.

The Data Architect/Data Governance Lead supports the Medicaid Enterprise Data Warehouse by leading data architecture, metadata management, and governance processes, ensuring compliance and scalability.

Lead the architecture and implementation of data integrations with healthcare payers, build data pipelines for AI/ML systems, and establish engineering best practices.

As a Lead Enterprise AI Data Engineer, you will design and build scalable data pipelines and AI tooling, enabling data-driven decision making in collaboration with business stakeholders and data scientists.

Lead and own the data science function: design and build production ML systems (recommendation, forecasting, segmentation), develop configurable multi-tenant models, run rigorous experimentation and causal analyses, connect model outputs to business impact, set scientific standards, and hire and mentor a high-velocity data science team.

Build and maintain Lead-to-Opportunity Salesforce solutions, integrations, and data enrichment workflows. Troubleshoot production lead capture/routing issues, document processes, and collaborate cross-functionally to support GTM pipeline creation and lifecycle engagement.

The Lead Data Engineer will develop a data service for command and control software, ensuring high-quality code and system integration while collaborating with various teams in a highly complex DoD context.

The Data Engineer will design and manage data pipelines, develop datasets for product metrics, and collaborate with teams to ensure robust data integration and processing.

The role involves building and optimizing data pipelines, modeling datasets, applying machine learning techniques, and supporting data strategies. Candidates should have experience with Python, data lakes, warehousing, and related technologies.

The Senior Data Engineer will build and deploy data pipelines, develop warehouse architectures, and design analytics solutions for the team.

As a Forward Deployed Engineer, you will design scalable data pipelines, optimize code for data processing, and collaborate with AI teams to meet their data needs.