Data engineering depth: 4+ years of industry experience, with production systems you have owned rather than contributed to
Programming and processing: strong engineering skills, ideally with distributed data processing (AWS, PySpark)
Scale: strong quantitative skills and experience estimating performance at high scale
Cloud and infrastructure: familiarity with cloud-based data services (AWS, RDS), containerised infrastructure (ECS, Docker), and data movement (batch, CDC, streamed and batch transformations)
AI fluency: our engineering team runs on an AI-native stack. You use AI tools day to day for writing code, documentation and pipeline logic, and you're comfortable building the data infrastructure that powers our ML and AI features. This is a genuine requirement, not a bonus line.
Ownership: self-motivated, with a strong sense of ownership over the systems and designs you build.
Responsibilities
Data engineering depth: 4+ years of industry experience, with production systems you have owned rather than contributed to
Programming and processing: strong engineering skills, ideally with distributed data processing (AWS, PySpark)
Scale: strong quantitative skills and experience estimating performance at high scale
Cloud and infrastructure: familiarity with cloud-based data services (AWS, RDS), containerised infrastructure (ECS, Docker), and data movement (batch, CDC, streamed and batch transformations)
AI fluency: our engineering team runs on an AI-native stack. You use AI tools day to day for writing code, documentation and pipeline logic, and you're comfortable building the data infrastructure that powers our ML and AI features. This is a genuine requirement, not a bonus line.
Ownership: self-motivated, with a strong sense of ownership over the systems and designs you build.
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