Data Architect Jobs in San Francisco, CA
Data Architect jobs in San Francisco are concentrated in SoMa, the Financial District, and Mission Bay, across fintech, enterprise software, healthcare technology, and cloud infrastructure. Companies actively hiring include Amazon Web Services, Meta, and OpenAI. See the openings below and apply to the ones that match your experience.
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Scaling - San Francisco
About the Team
OpenAI’s Industrial Compute team is building and productizing infrastructure capabilities that help organizations deploy and operate advanced AI systems at scale. The team works across AI hardware, systems engineering, physical infrastructure, and customer delivery to turn emerging technologies into reliable, repeatable infrastructure solutions.
Our work sits at the intersection of technical strategy, product development, engineering, and deployment. We partner closely with customers and internal engineering teams to solve complex infrastructure challenges spanning compute, power, cooling, controls, and facility efficiency.
About the Role
We are seeking a senior, hands-on Data Center Infrastructure Architect to develop and optimize the physical infrastructure required for large-scale AI deployments.
This is a broad technical role spanning data center architecture, electrical and mechanical systems, high-density compute, controls, telemetry, and digital modeling. You will use simulation, operational data, and digital-twin approaches to evaluate infrastructure designs, identify system-level constraints, and improve efficiency, reliability, cost, and speed of deployment.
The ideal candidate can move fluidly between first-principles analysis, facility and equipment design, computational modeling, engineering review, and real-world implementation. You should be comfortable working across disciplines rather than operating solely within electrical, mechanical, or software boundaries.
Key Responsibilities
Define system-level architectures for high-density AI data centers across power, cooling, IT equipment, controls, and facility infrastructure.
Develop digital twins and other computational models that represent the behavior of data center systems under changing workloads, environmental conditions, equipment configurations, and failure scenarios.
Use design and operational data to identify constraints, improve PUE and related efficiency metrics, and optimize capacity, reliability, water consumption, cost, and deployment schedules.
Evaluate tradeoffs across electrical topology, cooling architecture, rack density, redundancy, controls, maintainability, constructability, and operational complexity.
Translate evolving AI hardware requirements into practical facility, rack, power, and thermal architectures.
Establish reference architecturesance requirements, and validation methodologies that can be reused across customer deployments.
Partner with software, data, controls, hardware, mechanical, electrical, construction, commissioning, and operations teams to connect digital models with real infrastructure behavior.
Integrate telemetry from systems such as BMS, EPMS, DCIM, SCADA, equipment controllers, and IT hardware into modeling and optimization workflows.
Lead technical reviews of customer and partner designs, identify material risks, and recommend changes grounded in quantitative analysis.
Work with customers and delivery teams to adapt reference solutions to site-specific constraints while preserving performance, reliability, and efficiency objectives.
Support pilotsance testing, and post-deployment analysis to validate models and continuously improve infrastructure designs.
Help shape the technical roadmap for Industrial Compute’s physical-infrastructure products and engineering services.
Qualifications
Significant experience designing or optimizing hyperscale data centers, large mission-critical facilities, or comparable infrastructure systems.
Broad knowledge of data center electrical and mechanical systems, including power distribution, backup power, thermal management, liquid cooling, heat rejection, controls, and monitoring.
Experience making system-level design decisions across multiple engineering disciplines.
Experience developing or applying simulation, optimization, digital-twin, or physics-based modeling techniques to physical infrastructure.
Strong understanding of data center efficiency and performance metrics, including PUE, WUE, utilization, capacity, reliability, and total cost of ownership.
Experience working with operational telemetry and translating real-world system behavior into design improvements.
Ability to evaluate complex tradeoffs involving performance, reliability, cost, schedule, scalability, sustainability, and maintainability.
Demonstrated ability to lead technical work in ambiguous, rapidly changing environments.
Strong written and verbal communication skills, including the ability to explain complex engineering decisions to customers, executives, and cross-functional teams.
Bachelor’s degree in mechanical engineering, electrical engineering, systems engineering, applied physics, or a related technical discipline.
Preferred Skills
Experience with high-density GPU clusters and direct-to-chip liquid cooling.
Experience connecting facility models with workload, rack, server, or chip-level power and thermal behavior.
Familiarity with modeling or engineering tools such as Modelicas.
Experience with BMS, EPMS, DCIM, SCADA, PLCs, data historians, or industrial controls.
Experience developing reference designs or new infrastructure architectures within a hyperscaler, data center operator, advanced engineering organization, or major design consultancy.
Experience taking an infrastructure concept from modeling and prototype validation through deployment and operational feedback.
Advanced degree in an engineering or scientific discipline.
Success in the First Year
Establish a credible system-level model of power, cooling, compute, and facility behavior for priority Industrial Compute use cases.
Identify and validate meaningful opportunities to improve efficiency, capacity, reliability, or deployment cost.
Deliver reusable reference architectures and engineering methodologies for customer deployments.
Create a repeatable feedback loop connecting modeling, operational telemetry, commissioning results, and future design decisions.
Become a trusted technical partner to internal engineering teams, customers, and infrastructure delivery partners.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core the full spectrum of humanity.
We are an equal opportunity employeration, or other applicable legally protected characteristic.
Background checks for applicants will be administered in accordance with applicable lawation technology systems and related data security obligations.
To notify OpenAI that you believe this job posting is non-compliant. No response will be provided to inquiries unrelated to job posting compliance.
We are committed to providing reasonable accommodations to applicants with disabilities.
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Compensation
$360K – $530K + Offers Equity
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Find Data Architect JobsData Architect Job Market in San Francisco
Who's Hiring
- Amazon Web Services33

- Meta8

- OpenAI8

- Happen Bank8

- Colliers Engineering & Design8

Top Industries Hiring
- Education
- Technology & Software
Data Architect Jobs in San Francisco: Frequently Asked Questions
How do I get a data architect job in San Francisco?
Focus your search on SoMa and Mission Bay, where the highest density of data-driven companies operates, including fintech firms, cloud platforms, and digital health startups. Hands-on experience with cloud-native data stacks, real-time pipelines, and governance frameworks gives you a clear edge here. Candidates who can show production-scale work, not just design docs, move fastest through San Francisco hiring processes.
Which companies hire data architects in San Francisco?
Companies currently hiring data architects in San Francisco include Amazon Web Services, Meta, and OpenAI, per current listings on Migrate Mate as of September 2026. San Francisco's hiring mix skews toward high-growth technology companies, financial services platforms, and healthcare technology organizations, all of which run large, complex data ecosystems that require dedicated architect-level expertise.
Are there remote data architect jobs in San Francisco?
Yes, though not universally. Data architect work is largely analytical and design-focused, which makes it more remote-compatible than hands-on infrastructure roles. About 68% of data architect openings tied to San Francisco are remote or hybrid as of September 2026, with fully remote positions most common at mature cloud software companies headquartered in SoMa and the Financial District.
How can I get a data architect job in San Francisco with little or no experience?
The most realistic entry path in San Francisco is moving through a data engineering or analytics engineering role at one of the city's mid-size SaaS or fintech companies, where smaller teams give junior contributors architecture exposure early. Building a portfolio that demonstrates data modeling decisions, not just pipeline builds, accelerates the transition. Local employers in digital health and fintech frequently promote from within once you've proven design judgment on live systems.
Which industries hire the most data architects in San Francisco?
San Francisco data architect roles concentrate in Education and Technology & Software, based on current listings on Migrate Mate as of September 2026. These sectors drive local hiring because San Francisco's economy is built around technology platforms, financial data products, and health data infrastructure, all of which require architects who can operate at significant scale and comply with strict data governance standards.
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