Senior Backend Engineer, Context Hub

The Context Hub is a knowledge store within Oura's HealthOS platform. It ingests data from diverse sources, transforms it through both pipelines and LLM-based interpretation layers, and serves structured User Context. If Oura's intelligence systems are the brain, the Context Hub is the memory. This is an opportunity to be part of teams that are at the forefront of wearable technology, innovating and defining the future of Oura’s products through collaboration across different teams.

What You Will Do

This is a platform-building role. You'll design foundational data infrastructure that multiple product teams depend on - not ship features in isolation.

• Design data transformation pipelines that convert raw health signals, user inputs, and third-party data into structured, queryable context
• Architect event-driven ingestion using tools like Kinesis, EventBridge, and SQS - handling duplicate events, traffic spikes, and partial failures gracefully
• Define flexible data models and schemas for a hierarchical health context ontology that evolves as new context types emerge
• Build multi-consumer APIs and data contracts serving LLM-powered reasoning engines, mobile apps, and analytics pipelines through clean, well-documented interfaces
• Operate and design data storage at scale
• Create SDKs and integration surfaces that let other squads contribute and own contexts with minimal friction
• Collaborate effectively in a globally distributed, diverse, and cross-functional team, adapting as Oura evolves and new opportunities emerge.
• Own production quality end-to-end: testing, monitoring (Grafana, Prometheus, OpenTelemetry), alerting, and documentation

How We Build With AI

At Oura, AI is a core part of how we build software. We encourage senior engineers to use tools like Cursor, Claude Code, and GitHub Copilot to accelerate pipeline development, iterate on schema design, prototype integration patterns, and reduce boilerplate - so you can focus on the hard problems. All code is reviewed and owned by the engineer. AI is a multiplier, not a crutch. We're looking for engineers who view AI as a coworker in the development loop, not just autocomplete.

Requirements

• 5+ years building and operating production data systems at scale in Python
• Experience designing event-driven architectures - message brokers, event streams, CDC pipelines, with understanding of delivery guarantees, fan-out, and backpressure
• Deep experience with data transformation pipelines - ingesting from heterogeneous sources, normalizing, enriching, and serving with clear data contracts
• Strong technical grasp of AWS ecosystems with a focus on varied data storage solutions (RDS, DynamoDB, etc.), asynchronous messaging (SQS, EventBridge), and scalable compute environments like EKS and Lambda.
• A data-model-first mindset - you start with the shape of the data and access patterns before jumping to service architecture
• Experience building shared platforms or APIs that multiple teams depend on, with thoughtful versioning and self-service integration patterns
• Strong systems design instincts - you think in terms of data integrity, resilience, and failure modes before they become incidents
• Excellent communication - you explain your thinking before acting, ask incisive questions upfront, and articulate trade-offs clearly
• Have a team-oriented