Data Engineer Jobs in Palo Alto, CA
Data Engineer jobs in Palo Alto are concentrated in the Stanford Research Park, Downtown University Avenue corridor, and the California Avenue district, across venture-backed tech startups, established semiconductor firms, and enterprise AI companies. Employers hiring right now include Tesla, SpaceXAI, and Rivian. Scan the live roles below and apply to whichever ones fit.
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Showing 5 of 656+ Data Engineer jobs
About the Job
Flight data from across our aircraft fleet feeds a central data platform that engineering teams rely on to understand how the aircraft perform. Pivotal is seeking a Full Stack Data Engineer to build the internal applications that put that data directly in engineers' hands, so teams across the company can explore it and answer their own questions.
You will work across the whole stack — backend services over the data platform, web front ends that make large datasets explorable, dashboards, AI-assisted tooling that helps engineers interpret what they are looking at, and the packaging that puts analysis tools on an engineer's laptop. Your users sit down the hall, and requirements arrive as a conversation with someone who needs an answer rather than as a written spec, so this role is as much about understanding the question as it is about shipping the tool. It suits an engineer early in their career who wants ownership of real products and direct contact with the people building and flying the aircraft.
Responsibilities
Internal applications: Build and maintain the internal web applications engineers use to work with flight data — flight record browsers, query interfaces, and analysis tools over the warehouse.
Backend services: Develop Python services and HTTP APIs that query Athena and S3 and return results at interactive speed, including pagination and caching over large result sets.
Front end: Build interfaces that make large telemetry datasets explorable — sortable tables, filters, and time-series plots — for engineers who are never going to write SQL.
AI-assisted diagnosis: Build agent-based tooling over the data platform so engineers can ask questions in plain language and follow a symptom through to a root cause without hand-writing queries. Design these tools to return the underlying data and how it was derived, not just an answer, so an engineer can verify the result before acting on it.
Self-serve analysis: Turn recurring one-off analyses from the firmware, GNC, battery, and maintenance teams into supported, self-serve tools instead of scripts that only their author can run.
Data catalog: Generate the internal field guide and data catalog from the warehouse itself, so what engineers read cannot drift from what the tables actually contain — and keep that metadata machine-readable, so the tooling and agents built on top of it interpret results correctly.
Alert review: Build the review and triage surfaces for fleet health alerts — acknowledgment, history, and trend views — alongside the existing dashboards and Slack notifications.
Tooling distribution: Package and distribute analysis tooling so it runs on an engineer's laptop, Windows included, without someone walking them through an environment setup.
Ownership: Carry your work through deployment — tests, code review, CI/CD, and the monitoring that tells you when something breaks.
Collaboration: Sit with the engineering teams to scope what they actually need, and document what you ship so the next person can support it.
Qualifications
- 2+ years of professional experience building and shipping web applications, backend through front end
- Bachelor’s degree in Computer Science or a related technical discipline, or equivalent practical experience
- Proficient in Python, and comfortable writing SQL against a real database
- Experience with a modern JavaScript or TypeScript front-end framework (React, Vue, or similar)
- Has built and consumed HTTP APIs, including authentication, pagination, and error handling
- Comfortable with Git-based workflows, code review, and CI/CD
- Can sit with a non-software engineer, work out what they actually need, and ship it — most requirements here arrive as a conversation, not a spec
Preferred Qualifications
- AWS experience, particularly S3, Athena, Lambda, or API Gateway
- Worked with large or time-series datasets, and knows how to keep a UI responsive over millions of rows
- Charting and data visualization libraries; Grafana dashboard development
- Docker, and infrastructure as code with Terraform
- Has built with LLM APIs or agent frameworks — tool use, retrieval over structured data, and evaluating whether the output can be trusted
- Has built internal tools where the users are colleagues rather than customers
Familiarity/experience with one or more of the following:
- Aerospace, automotive, or robotics telemetry, and flight or vehicle test data
- Packaging desktop or command-line tooling for non-developer users, including on Windows
- Authentication and access control for internal applications (SSO, IAM, scoped credentials)
- Regulated or safety-critical environments with traceability and data retention requirements
- Natural-language or conversational interfaces over structured, time-series, or diagnostic data
- Interest in RC planes, quadcopters, or aviation
Attributes aligned with Core Values
- Safety Above All: Demonstrates a proactive safety mindset by embedding safety into daily work, identifying and mitigating risks, encouraging open dialogue about safety concerns, and continuously improving practices and protocols.
- Customer Focus: Puts customers at the center of every action by understanding their challenges, delivering meaningful value, and supporting their success.
- Work Better Together: Seeks and values diverse perspectives, builds cross-functional relationships, and fosters trust through empathetic, fact-based communication and commitment to shared decisions.
- Be the Pilot: Drives results with clarity and purpose by focusing on what matters most, adapting to change, taking initiative, and owning outcomes.
- Embrace the Unknown: Navigates ambiguity with resilience and bold thinking, challenges the status quo, and combines innovative ideas with practical approaches to overcome obstacles and drive progress.
- Respect for People: Fosters a high-performance culture grounded in respect, professionalism, and support by balancing high expectations with a healthy, collaborative environment and being a trusted, dependable teammate.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
About the Job
Flight data from across our aircraft fleet feeds a central data platform that engineering teams rely on to understand how the aircraft perform. Pivotal is seeking a Full Stack Data Engineer to build the internal applications that put that data directly in engineers' hands, so teams across the company can explore it and answer their own questions.
You will work across the whole stack — backend services over the data platform, web front ends that make large datasets explorable, dashboards, AI-assisted tooling that helps engineers interpret what they are looking at, and the packaging that puts analysis tools on an engineer's laptop. Your users sit down the hall, and requirements arrive as a conversation with someone who needs an answer rather than as a written spec, so this role is as much about understanding the question as it is about shipping the tool. It suits an engineer early in their career who wants ownership of real products and direct contact with the people building and flying the aircraft.
Responsibilities
Internal applications: Build and maintain the internal web applications engineers use to work with flight data — flight record browsers, query interfaces, and analysis tools over the warehouse.
Backend services: Develop Python services and HTTP APIs that query Athena and S3 and return results at interactive speed, including pagination and caching over large result sets.
Front end: Build interfaces that make large telemetry datasets explorable — sortable tables, filters, and time-series plots — for engineers who are never going to write SQL.
AI-assisted diagnosis: Build agent-based tooling over the data platform so engineers can ask questions in plain language and follow a symptom through to a root cause without hand-writing queries. Design these tools to return the underlying data and how it was derived, not just an answer, so an engineer can verify the result before acting on it.
Self-serve analysis: Turn recurring one-off analyses from the firmware, GNC, battery, and maintenance teams into supported, self-serve tools instead of scripts that only their author can run.
Data catalog: Generate the internal field guide and data catalog from the warehouse itself, so what engineers read cannot drift from what the tables actually contain — and keep that metadata machine-readable, so the tooling and agents built on top of it interpret results correctly.
Alert review: Build the review and triage surfaces for fleet health alerts — acknowledgment, history, and trend views — alongside the existing dashboards and Slack notifications.
Tooling distribution: Package and distribute analysis tooling so it runs on an engineer's laptop, Windows included, without someone walking them through an environment setup.
Ownership: Carry your work through deployment — tests, code review, CI/CD, and the monitoring that tells you when something breaks.
Collaboration: Sit with the engineering teams to scope what they actually need, and document what you ship so the next person can support it.
Qualifications
- 2+ years of professional experience building and shipping web applications, backend through front end
- Bachelor’s degree in Computer Science or a related technical discipline, or equivalent practical experience
- Proficient in Python, and comfortable writing SQL against a real database
- Experience with a modern JavaScript or TypeScript front-end framework (React, Vue, or similar)
- Has built and consumed HTTP APIs, including authentication, pagination, and error handling
- Comfortable with Git-based workflows, code review, and CI/CD
- Can sit with a non-software engineer, work out what they actually need, and ship it — most requirements here arrive as a conversation, not a spec
Preferred Qualifications
- AWS experience, particularly S3, Athena, Lambda, or API Gateway
- Worked with large or time-series datasets, and knows how to keep a UI responsive over millions of rows
- Charting and data visualization libraries; Grafana dashboard development
- Docker, and infrastructure as code with Terraform
- Has built with LLM APIs or agent frameworks — tool use, retrieval over structured data, and evaluating whether the output can be trusted
- Has built internal tools where the users are colleagues rather than customers
Familiarity/experience with one or more of the following:
- Aerospace, automotive, or robotics telemetry, and flight or vehicle test data
- Packaging desktop or command-line tooling for non-developer users, including on Windows
- Authentication and access control for internal applications (SSO, IAM, scoped credentials)
- Regulated or safety-critical environments with traceability and data retention requirements
- Natural-language or conversational interfaces over structured, time-series, or diagnostic data
- Interest in RC planes, quadcopters, or aviation
Attributes aligned with Core Values
- Safety Above All: Demonstrates a proactive safety mindset by embedding safety into daily work, identifying and mitigating risks, encouraging open dialogue about safety concerns, and continuously improving practices and protocols.
- Customer Focus: Puts customers at the center of every action by understanding their challenges, delivering meaningful value, and supporting their success.
- Work Better Together: Seeks and values diverse perspectives, builds cross-functional relationships, and fosters trust through empathetic, fact-based communication and commitment to shared decisions.
- Be the Pilot: Drives results with clarity and purpose by focusing on what matters most, adapting to change, taking initiative, and owning outcomes.
- Embrace the Unknown: Navigates ambiguity with resilience and bold thinking, challenges the status quo, and combines innovative ideas with practical approaches to overcome obstacles and drive progress.
- Respect for People: Fosters a high-performance culture grounded in respect, professionalism, and support by balancing high expectations with a healthy, collaborative environment and being a trusted, dependable teammate.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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Find Data Engineer JobsData Engineer Job Market in Palo Alto
Who's Hiring
- Tesla253
- SpaceXAI60
- Rivian45
- Pivotal30
- Amazon30
Top Industries Hiring
- Technology & Software45
- Retail15
- Manufacturing15
- Accounting & Auditing15
- Banking & Financial Services15
Data Engineer Jobs in Palo Alto: Frequently Asked Questions
How do I get a data engineer job in Palo Alto?
The most direct path is targeting Palo Alto's deep bench of AI and machine learning companies, semiconductor firms, and enterprise SaaS platforms concentrated in Stanford Research Park and along Page Mill Road. Candidates with hands-on pipeline experience in cloud-native environments and familiarity with large-scale model training data stand out. Connecting with Stanford's industry partnership network also opens doors to research-adjacent roles that rarely surface elsewhere.
Which companies hire data engineers in Palo Alto?
Companies currently hiring data engineers in Palo Alto include Tesla, SpaceXAI, and Rivian, per current listings on Migrate Mate as of September 2026. Palo Alto's employer mix leans heavily toward deep-tech and AI-focused organizations, alongside a strong cluster of financial technology and healthcare analytics firms with offices in the area.
Are there remote data engineer jobs in Palo Alto?
Yes, though roles tied to proprietary infrastructure or on-site data centers tend to require in-person presence. About 42% of data engineer openings tied to Palo Alto are remote or hybrid as of September 2026, reflecting how broadly analytical and pipeline work can be done off-site. The most fully remote openings typically involve cloud data platform work rather than hands-on hardware or lab-adjacent data engineering.
How can I get a data engineer job in Palo Alto with little or no experience?
The most realistic entry point is an associate or junior data analyst role at one of Palo Alto's mid-stage startups, which often hire for data generalists who grow into engineering work. Companies in Stanford Research Park with active university partnerships frequently recruit candidates who have completed Stanford or SLAC-affiliated research projects involving real datasets. Building a portfolio with public cloud certifications and open-source pipeline contributions sharpens your profile for these employers specifically.
Which industries hire the most data engineers in Palo Alto?
Most data engineer openings in Palo Alto sit in Technology & Software, Retail, and Manufacturing, per current listings on Migrate Mate as of September 2026. Palo Alto's role as a hub for AI research commercialization, semiconductor design, and health technology drives outsized demand across those sectors compared to most other California cities.
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