Senior Data Platform Engineer – Self-Serve Data Tools

Affirm; Boise (ID); On-site; USD 173,000 - 255,000; Full time

Benefits offered by this job

Health care coverage

Flexible Spending Wallets

Time off

ESPP

Job summary

Affirm is building a scalable data platform to power its turnkey credit offerings. You will design and operate core platform capabilities, enabling engineers to build, deploy, and run data applications at scale while maintaining strong security and governance.

You will own integrations, improve observability, and help shape the roadmap toward agentic data tooling and AI-ready workflows across OLTP/OLAP infrastructure.

Qualifications

• Hands-on experience operating a cloud analytical data platform with governance and RBAC.
• Strong Python and SQL for building reliable platform services.
• Experience with containerized deployments and CI/CD tooling.
• Ability to own on-call incidents and provide durable fixes.

Responsibilities

• Design and implement platform features for data applications at scale.
• Build integrations with CI/CD, identity management, external APIs.
• Strengthen data access governance with RBAC and secure views.
• Automate toil and enable self-service for platform users.
• Improve observability, telemetry, alerting, and incident response.
• Participate in on-call coverage and platform maintenance.
• Contribute to the evolving platform roadmap with AI-native tooling.
• Maintain high code quality through reviews and operational readiness.

Skills

Python

SQL

Operational ownership

Observability

Communication

Education

Bachelor’s degree in a related field

Tools

Docker

Buildkite

GitHub Actions

Job description

Affirm is building a scalable data platform to power its turnkey credit offerings. You will design and operate core platform capabilities, enabling engineers to build, deploy, and run data applications at scale while maintaining strong security and governance.

You will own integrations, improve observability, and help shape the roadmap toward agentic data tooling and AI-ready workflows across OLTP/OLAP infrastructure.