Data Engineer

🎯 The role We're looking for an experienced engineer to join our Platform team and help build the data systems that underpin Originator Inc.'s AI development and production-like environments. The team works on problems across large-scale data ingestion, transformation, de-identification, data quality, platform infrastructure and integration with AI systems. The work is highly hands-on, but we're also looking for people who can take ownership of ambiguous problem spaces, shape architecture and influence technical direction beyond their immediate area. 🚀 What you'll work on • Design and build reliable, scalable data systems that ingest and process large, heterogeneous datasets from internal and third-party sources. • Own significant technical problems from initial ambiguity through architecture, implementation and production operation. • Build pipelines and data-processing systems that clean, transform, validate and prepare data for downstream AI and engineering use cases. • Design approaches for handling sensitive data, including de-identification, anonymisation and PII removal, while preserving useful relationships across datasets. • Improve data quality, reliability, observability, security and performance as our systems and datasets scale. • Build tooling and infrastructure that makes acquired and internal datasets easier for engineers and AI teams to use safely and consistently. • Work closely with engineers across Platform, AI and product-facing teams on problems that sit across traditional software, data and infrastructure boundaries. • Help make architectural decisions where there may not yet be an established pattern or obvious answer. • Remain deeply hands-on while providing technical direction, mentoring other engineers and helping teams make strong engineering decisions. • Identify opportunities to simplify systems, automate manual processes and improve how data moves through the organisation. 🚀 What you'll work on • Design and build reliable, scalable data systems that ingest and process large, heterogeneous datasets from internal and third-party sources. • Own significant technical problems from initial ambiguity through architecture, implementation and production operation. • Build pipelines and data-processing systems that clean, transform, validate and prepare data for downstream AI and engineering use cases. • Design approaches for handling sensitive data, including de-identification, anonymisation and PII removal, while preserving useful relationships across datasets. • Improve data quality, reliability, observability, security and performance as our systems and datasets scale. • Build tooling and infrastructure that makes acquired and internal datasets easier for engineers and AI teams to use safely and consistently. • Work closely with engineers across Platform, AI and product-facing teams on problems that sit across traditional software, data and infrastructure boundaries. • Help make architectural decisions where there may not yet be an established pattern or obvious answer. • Remain deeply hands-on while providing technical direction, mentoring other engineers and helping teams make strong engineering decisions. • Identify opportunities to simplify systems, automate manual processes and improve how data moves through the organisation. 🚀 What you'll work on • Design and build reliable, scalable data systems that ingest and process large, heterogeneous datasets from internal and third-party sources. • Own significant technical problems from initial ambiguity through architecture, implementation and production operation. • Build pipelines and data-processing systems that clean, transform, validate and prepare data for downstream AI and engineering use cases. • Design approaches for handling sensitive data, including de-identification, anonymisation and PII removal, while preserving useful relationships across datasets. • Improve data quality, reliability, observability, security and performance as our systems and datasets scale. • Build tooling and infrastructure that makes acquired and internal datasets easier for engineers and AI teams to use safely and consistently. • Work closely with engineers across Platform, AI and product-facing teams on problems that sit across traditional software, data and infrastructure boundaries. • Help make architectural decisions where there may not yet be an established pattern or obvious answer. • Remain deeply hands-on while providing technical direction, mentoring other engineers and helping teams make strong engineering decisions. • Identify opportunities to simplify systems, automate manual processes and improve how data moves through the organisation.