Top Data Engineering Jobs in San Francisco, CA

As a Senior Software Engineer in Data Engineering, you'll build scalable data pipelines, design schemas, ensure data quality, and collaborate with teams to produce insights, thus enhancing decision-making throughout the fintech sector.

Build and support cloud-based data engineering solutions using Java, Scala, Python, databases, data warehouses, distributed computing, and streaming technologies. Collaborate with Agile engineering and product teams, develop and test full-stack solutions, optimize performance, review code, stay current with technology trends, and mentor other engineers. The role requires application development, big data, and cloud computing experience.

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.

Designs, builds, and maintains scalable data pipelines, ETL workflows, and data products. Partners with analytics, product, and engineering teams to create reliable, testable, user-friendly data systems. Works on data modeling, cloud data platforms, CI/CD, observability, monitoring, performance optimization, and AI-enabled automation for data engineering.

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.

Contract Marketing Data Analyst to clean and rebuild marketing data models, integrate ad/analytics and conversation data into Snowflake, own dbt + Sigma layers and dashboards, implement quality-control/health monitoring, align GTM data models, and produce a plain-language data dictionary for team and LLM use.

Lead development of Azure-based data and AI platforms, APIs, and cloud services for Microsoft’s AI initiatives. Build GenAI, LLM, RAG, semantic search, vector database, and agentic AI solutions. Create reusable frameworks and platform services, establish responsible AI and governance practices, and provide hands-on technical leadership through architecture reviews, coding, mentoring, and cross-functional partnership.

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.

Lead enterprise data architecture across batch and streaming platforms, warehouses, lakehouses, OLAP, OLTP, and AI-enabled use cases. Establish standards for data quality, governance, lineage, access control, cataloging, observability, and SLAs. Serve as a technical escalation point, influence cross-functional roadmaps, guide AI-supported workflows, and mentor engineers through hands-on technical leadership.

Leads and develops a data engineering team supporting People Analytics and AI-assisted workforce analytics. Designs scalable data models, pipelines, semantic layers, testing frameworks, and governance practices using modern cloud data technologies. Remains hands-on with production code, technical reviews, and critical workflows while partnering with analysts, data scientists, People leaders, Legal, and Compliance. Ensures sensitive workforce data is reliable, secure, observable, and compliant.