- Build and maintain data pipelines - batch and streaming - that process 3B+ daily events
- 3 - 5 years in backend or infrastructure engineering,with production experience - not just side projects.
Strong
Python
- Valuable to have
- Experience with real-time streaming architectures.
Responsibilities
- Build and maintain data pipelines - batch and streaming - that process 3B+ daily events. Reliability and cost matter as much as throughput.
- Work on network infrastructure - service connectivity, internal networking abstractions, and ensuring secure, reliable communication across our platform services.
- Own pieces of ML pipeline infrastructure: data ingestion, feature pipelines, and model serving infra that supports our recommendation and personalisation systems.
- Work on platform problems: container orchestration, infra-as-code, and internal abstractions that let product teams move without depending on infra for every deploy.
- Set up and improve our observability stack. Define SLIs, own alerts, reduce MTTR. If something breaks, we want to know before users do.
- Contribute to CI/CD reliability: faster builds, cleaner rollouts, fewer incidents at deploy time.
- Contribute to internal AI tooling - building or integrating tools that improve developer productivity and automate repetitive infra and data workflows.
Requirements
- 3 - 5 years in backend or infrastructure engineering, with production experience - not just side projects
- Strong Python; you write code that runs in production, not just automation scripts
- Real experience with distributed data processing and streaming systems in a production environment
- Hands-on with container orchestration and infra-as-code tooling
- Familiarity with observability tooling - metrics, logs, traces
- Some understanding of ML pipelines - you dont need to be an ML engineer, but knowing how data flows from raw to model matters here
Good to have
- Experience with real-time streaming architectures
- Exposure to ML infrastructure, feature stores, or data governance
- Prior work in a consumer product company where data volumes are high and latency budgets are tight
- Contributions to open-source infra or data tooling
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