Top Data Engineer Jobs in San Francisco Bay Area, CA

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Develop and maintain data infrastructure for safety data analysis, build scalable data pipelines, conduct exploratory analyses, and support regulatory reporting in AV safety.

Own and scale ReadyOn's data platform: design/build real-time and batch pipelines, implement ingestion and transformation workflows, model schemas in Snowflake and PostgreSQL, ensure observability and data quality, build connectors and feature-store roadmap, and provide technical leadership and mentorship across data, AI, and backend teams.

As a Data Engineer, you will architect and maintain the data systems, create pipelines and integrations, and ensure data quality for analytics and reporting.

The Data Engineer will build and scale Baseten's internal data platform, designing data models and pipelines while enabling self-service analytics and ensuring data reliability.

As an Analytics Engineer, you will create scalable solutions for data reporting, build data pipelines, and establish high data integrity standards while collaborating with multiple teams.

Design, implement, and maintain scalable data infrastructure and real-time processing for analytics platforms. Migrate systems from AWS to on-prem Kafka/Spark, implement data transformations, support production, optimize pipelines, query and analyze data, and build scripts and APIs to operationalize data science work.

Lead design and implementation of a scalable Big Data Audience Management platform (500+ TB). Own data pipelines (ingest, validation, transformation, storage) using Spark and Scala, mentor engineers, perform R&D on new technologies, and partner with product managers to meet roadmap and business needs.

As a Data Engineer at Promise, you'll design data architectures, automate workflows, enforce data quality, and support analytics, enabling data-driven decisions.

The Data Engineer will support Data Analytics and Business Intelligence efforts, developing data pipelines, ensuring data performance, and collaborating within Agile frameworks to meet complex requirements for federal clients.

As a Data Engineer, you will build and own the data infrastructure, design pipelines, ensure data quality, and implement DataOps practices while collaborating closely with product and engineering teams.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Own and build the end-to-end data layer: extraction pipelines from messy supplier emails and PDFs, the unified data model, reliable observable pipelines, and ML-driven extraction evaluation. Drive extraction accuracy, data quality, lineage, monitoring, and automation to scale coverage and enable engineering and product to depend on the data layer.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

The Senior Staff Data Engineer will design scalable data systems, lead technical direction, optimize data platforms, and ensure data quality and performance for Observability & Insights.

Build user-facing data products by transforming internal data into actionable metrics. Collaborate with platform teams and data scientists to prototype, validate, and prioritize data sources. Write requirements and proposals, engage in user research, and deliver performant Python/SQL-based data features using Databricks and Postgres.

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.

In this role, you'll build and scale high-throughput data pipelines, ensure data quality, and leverage AI for improved data operations. You'll also lead initiatives in a fast-paced environment focused on real estate data engineering.

Design, develop, integrate, test, and optimize scalable data integration solutions and pipelines for the OneGL finance environment. Support production users and month-end close processes, troubleshoot issues, develop test scripts, and document architectures. Collaborate with technical and business stakeholders, perform gap analysis, and support implementation and post-implementation activities.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The Founding Data Engineer will manage the company's data models, ensure metric integrity, and work with various teams to deliver reliable metrics and reporting systems.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Build and own the core data models and pipelines connecting product usage, accounts, revenue, billing, and customer health. Create trusted metrics, dashboards, and self-serve reporting. Partner with Product, Engineering, and GTM teams to improve data quality, design AI-enabled workflows and agents, and support leadership with accurate visibility into adoption, pipeline, retention, expansion, and revenue.

Lead design, build, and optimization of large-scale real-time and batch data ingestion and transformation pipelines. Integrate enterprise systems (SFDC, NetSuite, Workday), ensure data quality, observability, and reliability, and enable analytics/AI workflows. Architect solutions using Airflow, dbt, Python, Kafka, Snowflake, apply DevOps/CI-CD practices, mentor global teams, manage multiple projects, and align data engineering initiatives with product and business stakeholders.