Staff Platform Architect, Data & AI
- Role
- Data Engineering
- Experience
- Staff
- Employment
- Full-time
- Company size
- Enterprise
Open to US only. Set where you work from to check your eligibility.
No BS summary
Staff-level platform architect with 10+ years in software engineering focused on enterprise data platforms, analytics infrastructure, and AI/ML systems. Must have MLOps, data modeling, AI agent-based architectures, federated catalogs, cloud-native infrastructure, governance, and AWS/public cloud experience. Regulated data-domain background such as credit risk or financial services is required.
Core skills
Required skills
Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries.
About the Role:
We are looking for a Staff Platform Architect to join our Data & AI Platform Architecture team. We are a small, high-use group that shapes technology strategy across analytics products, AI/ML enablement, and data infrastructure at enterprise scale.
This is a role for someone with deep fundamentals in data, analytics, and MLOps platforms. You should also know how to evolve them to serve both humans and AI agents, internal and external, with equal thoughtfulness.
You will extend and evolve a set of existing platforms including our MLOps infrastructure, batch platform, analytics stack, and managed analytics offerings, while leading greenfield design of our AI-ready data foundation. You will report to the Sr. Director of Platform Engineering. This is an individual contributor position.
What you'll do here
- Evolve our existing batch, analytics and MLOps platforms improving reliability, cost, and operational efficiency.
- Develop the infrastructure for our semantic and ontology layers. (including authoring and governance tooling, lifecycle management, and catalog integration)
- Design the usage infrastructure, including APIs, libraries, and services. This infrastructure should make these layers usable by any downstream consumer, such as internal users, client-facing products, and AI agents. The infrastructure should also include permission-aware semantic discovery.
- Guide technology adoption across engineering teams by making the right architectural choices well-reasoned and easy to follow.
- Lead focused prototyping and R&D efforts with analytics product and engineering teams to validate new AI and analytics capabilities before broader platform investment.
- Mentor engineers across the organization in your areas of expertise, with a focus on first-principles thinking, system design, and product awareness.
- 10+ years of software engineering experience, with a deep focus on data platforms, analytics infrastructure, and AI/ML systems at enterprise scale.
- Bachelor's Degree or higher in science, technology, engineering or related field
- Experience building or operating MLOps platforms from data access and feature engineering through model deployment and monitoring.
- Experience with data modeling, schema design, and data quality as platform engineering concerns.
- Hands-on experience with AI agent-based architectures for governed data access, semantic discovery, and retrieval over enterprise data assets.
- Experience designing federated catalog architectures that deliver unified data access across platforms and data silos.
- Experience with security, compliance and governance considerations for AI/ML workloads, including data residency and access control.
- Experience with distributed computing, cloud-native infrastructure, and the cost and operational dynamics of running large-scale data workloads on public clouds such as AWS.
- Comfort with infrastructure as code and operating production workloads
- Demonstrated experience managing and evolving shared platform architectures over time.
- Experience driving architectural agreement across teams through technical influence, reference architectures, and hands-on collaboration, rather than through formal authority alone.
- Background in credit risk, financial services, or other regulated data domains where governance and compliance constraints shape platform design.
Benefits/Perks:
- Great compensation package and bonus plan
- Core benefits including medical, dental, vision, and matching 401K
- Flexible work environment, ability to work remote, hybrid or in-office
- Flexible time off including volunteer time off, vacation, sick and 12-paid holidays
- #LI-Remote
Our uniqueness is that we celebrate yours. Experian's people first, inclusive and purpose driven culture is multi award-winning; World's Best Workplaces™ 2025 (Fortune Global Top 25), Great Place To Work™ in 26 countries to name a few. Check out Experian Life on social or explore our Careers Site to understand why.
Our compensation reflects the cost of labor across several U.S. geographic markets. The base pay range for this position is listed above. Within this range, individual pay is determined by work location and additional factors such as job-related skills, experience, and education. This position is also eligible for a variable pay opportunity and a comprehensive benefits package.
Recruitment Fraud Awareness - Experian's recruitment process is conducted only through authorised channels. Recruitment communications will only be sent from an @experian.com email address. Experian will never ask candidates to make any payment as part of an application, interview, assessment, onboarding, or recruitment process. To apply for roles or verify opportunities, please visit experian.com/careers.
Experian is proud to be an Equal Opportunity Employer for all groups protected under applicable federal, state and local law, including protected veterans and individuals with disabilities. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
What you'll do
- Evolve our existing batch, analytics and MLOps platforms improving reliability, cost, and operational efficiency.
- Develop the infrastructure for our semantic and ontology layers, including authoring and governance tooling, lifecycle management, and catalog integration.
- Design the usage infrastructure, including APIs, libraries, and services, making these layers usable by downstream consumers such as internal users, client-facing products, and AI agents.
- Include permission-aware semantic discovery in the usage infrastructure.
- Guide technology adoption across engineering teams by making the right architectural choices well-reasoned and easy to follow.
- Lead focused prototyping and R&D efforts with analytics product and engineering teams to validate new AI and analytics capabilities before broader platform investment.
- Mentor engineers across the organization in areas of expertise, with a focus on first-principles thinking, system design, and product awareness.
What they require
- 10+ years of software engineering experience, with a deep focus on data platforms, analytics infrastructure, and AI/ML systems at enterprise scale.
- Bachelor's Degree or higher in science, technology, engineering or related field.
- Experience building or operating MLOps platforms from data access and feature engineering through model deployment and monitoring.
- Experience with data modeling, schema design, and data quality as platform engineering concerns.
- Hands-on experience with AI agent-based architectures for governed data access, semantic discovery, and retrieval over enterprise data assets.
- Experience designing federated catalog architectures that deliver unified data access across platforms and data silos.
- Experience with security, compliance and governance considerations for AI/ML workloads, including data residency and access control.
- Experience with distributed computing, cloud-native infrastructure, and the cost and operational dynamics of running large-scale data workloads on public clouds such as AWS.
- Comfort with infrastructure as code and operating production workloads.
- Demonstrated experience managing and evolving shared platform architectures over time.
- Experience driving architectural agreement across teams through technical influence, reference architectures, and hands-on collaboration, rather than through formal authority alone.
- Background in credit risk, financial services, or other regulated data domains where governance and compliance constraints shape platform design.
Benefits
- Great compensation package and bonus plan.
- Core benefits including medical, dental, vision, and matching 401K.
- Flexible work environment, ability to work remote, hybrid or in-office.
- Flexible time off including volunteer time off, vacation, sick and 12-paid holidays.
- Explore all our exciting benefits here: https://yourexperianbenefits.com/cand-index.html
- Variable pay opportunity.
- Comprehensive benefits package.
Irish company that provides information services
What people say about this company
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