Data Platform Engineer – Large-Scale Ingestion & PII

Dex; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Dex is partnering with a seed-stage research lab in London/Paris to recruit a role that will own the data platform powering AI training environments. You will design, implement, and operate end-to-end ingestion, processing, and storage systems, focusing on production code and scalable architectures rather than dashboards.

You will build automated validation, sanitisation, and PII-redaction pipelines, making raw data immediately usable for AI training. Hybrid work from London or Paris is expected.

Qualifications

• Proven track record designing, shipping, and operating substantial production data systems end to end.
• Ability to debug complex, distributed failure modes in unfamiliar codebases.
• Experience with large, messy datasets and data ingestion bottlenecks.
• Capacity to define system architecture from ambiguous requirements and ship production systems.
• Hybrid work capability from London or Paris.

Responsibilities

• Architect and operate scalable ingestion, processing, and storage systems for high-volume datasets.
• Build automated validation, sanitisation, and PII-redaction pipelines for AI training data.
• Balance storage, latency, observability, reliability, and cloud costs with pragmatic trade-offs.
• Establish reusable data platform patterns for use by software and AI engineers.
• Debug production failures across the stack and deploy modern tooling to improve feedback loops.

Skills

Data platforms

Distributed systems

Data ingestion

PII sanitisation

Observability

Production debugging

Cloud cost awareness

Job description

Dex is partnering with a seed-stage research lab in London/Paris to recruit a role that will own the data platform powering AI training environments. You will design, implement, and operate end-to-end ingestion, processing, and storage systems, focusing on production code and scalable architectures rather than dashboards.

You will build automated validation, sanitisation, and PII-redaction pipelines, making raw data immediately usable for AI training. Hybrid work from London or Paris is expected.