Staff Software Engineer - Data Foundation
Job Description
[Up to c. $300k/£200k + Equity | Fully Remote - East Coast US & Europe]
Role Overview
We’re representing a high-growth technology organisation whose product is built around a large-scale data platform. They are looking for a Staff Software Engineer to help shape the foundations that power data ingestion, processing and storage across the business.
This is a hands-on software engineering position with significant architectural ownership. You’ll work across a modern stack including Go, Kotlin, Rust and Python, making pragmatic technology choices based on the problem rather than defaulting to a familiar solution. The role will suit an engineer who enjoys taking complex, ambiguous problems, establishing the right technical direction and turning them into reliable systems that perform at meaningful scale...
Role Snapshot
- Bring strong software engineering experience and excellent Computer Science fundamentals, with the ability to adapt quickly across technologies and complex problem spaces
- Translate product requirements into robust technical solutions, defining domains, interfaces and workflows while making key architectural decisions across the data platform
- Design and build distributed data systems that prioritise correctness, scalability and performance across large-scale ingestion, processing and data lake storage
- Break complex technical problems into clear, manageable work for other engineers, while taking ownership of major components from initial design through to production
- Work comfortably across Go, Kotlin, Rust and Python, selecting the right technology for the problem and applying first-principles thinking to system design, failure modes and scaling challenges
- Operate effectively in an ambiguous, fully distributed environment, communicating clearly with product, customers and engineering teams while mentoring other engineers and raising technical standards
- Use AI development tools effectively while maintaining strong engineering judgement around their limitations and failure modes
- (Preferred) Experience with data lakes and formats such as Parquet, Iceberg or Delta Lake, or working in an organisation where data is central to the product
...
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