Top Data Engineering Jobs in San Francisco, CA

Designs and builds scalable ETL infrastructure, trusted core datasets, data pipelines, automated workflows, and self-service data tools. Partners with data scientists and business stakeholders to define requirements, monitor pipelines, establish delivery SLAs, reduce operational costs, and support product and business decision-making. The role requires strong SQL, data modeling, cloud data platform, object-oriented programming, and AI-enabled data development expertise.

Build and operate scalable batch and streaming data platform infrastructure, including ETL/ELT frameworks, data quality, governance, lineage, schema management, access controls, and observability. Own the technical roadmap, architecture, reliability, and incident response for core platform systems. Partner with engineering, analytics, data science, and marketing teams on data contracts and domain onboarding. Mentor engineers and participate in an on-call rotation while ensuring compliance with fintech requirements.

Twelve-week Data Engineer internship supporting Coinbase’s Platform product group. Responsibilities include building and maintaining data pipelines, developing scalable cloud-based infrastructure, integrating internal and external data sources, supporting analytical deep dives, documenting findings, and applying data governance, security, and performance optimization practices. The role uses Python, SQL, cloud platforms, and AI/API technologies while collaborating with senior engineers and cross-functional partners.

The Senior Data Engineer will design, implement, and maintain data infrastructure and ETL pipelines, optimize data flow, and develop reporting solutions. They will collaborate with teams to enhance data quality and support data-driven decision-making.

Define enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, OLTP, OLAP, AI, and vector-store use cases. Establish standards for data quality, governance, lineage, access, observability, SLAs, and modeling. Serve as a technical escalation point, influence roadmaps, implement AI-enabled workflows, and mentor engineers through hands-on architecture, coding, reviews, and technical guidance.

This job posting provides no substantive details beyond the title. The Data Engineering Manager role likely relates to overseeing data engineering activities, but specific responsibilities and qualifications are not provided.

Leads an eight-person analytics engineering team responsible for semantic models, governed metrics, curated datasets, enterprise BI, AI-enabled data access, and production data applications. Sets data strategy, builds evaluation and telemetry systems for LLM-powered workflows, establishes engineering and governance standards, manages roadmaps, and partners cross-functionally to drive self-service analytics. Hires, coaches, and develops team members while ensuring trusted, secure, scalable data products.

Design and maintain reliable data pipelines and computed tables using SparkSQL and PySpark. Ingest, transform, and integrate structured and unstructured data from IoT devices, software products, external datasets, and customer metadata. Deliver high-quality data for analytics, dashboards, causal inference, and machine learning. Collaborate with data scientists, analysts, AI/ML teams, and engineers while ensuring strong uptime, scalability, and data model quality.

Build and maintain scalable, secure data pipelines, models, and platforms that enable analytics teams and AI applications to access trusted enterprise data. Partner with marketing, product, and analytics stakeholders to translate requirements into technical solutions, establish data governance and access controls, and independently deliver projects from design through production. Support AI-driven decision-making while ensuring reliability, compliance, and operational efficiency.

Designs and maintains production data pipelines, dbt models, Airflow workflows, and People Analytics datasets using Snowflake and Workday data. Owns data quality, observability, row-level security, governance, and access controls for sensitive People data. Builds semantic models and AI agents, collaborates with analysts and stakeholders, translates requirements into reliable deliverables, and mentors engineers through code reviews and technical guidance.