Director of Data Engineering

Role Overview

• Own the technical design of Nebula — Vibe's identity graph, entity resolution pipelines, and data clean room integrations with partners including major broadcasters.
• Define the end-to-end data architecture serving Analytics, ML, and real-time bidding systems.
• Solve training vs. inference skew: ensure data used to train models matches data available at bid time.
• Design and ship the ML platform — feature stores, model registries, and CI/CD for ML — so data scientists can deploy models to production without infrastructure blockers.
• Own the "golden path": a data scientist pushes code, a model retrains and deploys automatically.
• Bridge the gap between Data Engineering and Data Science; remove friction, not just document it.
• Champion the transition to a security-first culture — SOC2, RBAC, PII anonymization — without turning compliance into a bottleneck.
• Build guardrails, not gatekeepers: automated policy checks that let engineers ship fast and safely.
• Own data retention policies, access controls, and governance frameworks across 200+ data assets.
• Hold the Data Platform P&L — track unit economics, separate storage costs from ML training costs, and ensure spend scales with revenue rather than ahead of it.
• Optimise across a hybrid stack: high-volume streaming (Kafka/Kinesis), log storage (S3/Athena), and GPU compute for ML training.
• Identify waste fast; distinguish inefficiency from intentional growth investment.
• Manage and grow the Data Platform Engineering team.
• Assess current team capabilities against what's needed to ship Nebula and the ML platform.
• Build a culture where engineers adopt structure because it makes them faster, not because they're told to.

Requirements

• Hands-on experience architecting large-scale data platforms — you've designed systems ingesting TBs of data, not just managed teams that did.
• Deep knowledge of data governance and compliance frameworks — CCPA, GDPR, SOC2 — and a track record of implementing them without killing engineering velocity.
• Experience with identity resolution, device graphs, or privacy-safe data matching (clean rooms, entity resolution).
• Strong understanding of the ML lifecycle: data prep, training, deployment, monitoring — and the infrastructure that makes it work at scale.
• Experience owning cloud infrastructure costs and optimising unit economics across AWS or GCP.
• Prior experience in a regulated data environment — you understand publisher contracts, DPAs, and what data you can and cannot use.

Tech Stack

• AWS
• Cloud
• Google Cloud Platform
• Kafka

Benefits

• Equity — Employee Stock Ownership Plan. You're building this; you should own part of it.
• Variable pay — based on objectives you hit. No arbitrary targets.
• Hybrid flexibility — We're in the heart of Paris and our team is in 3x a week.
• Health insurance — Full coverage via Alan.
• Meal vouchers — Via Swile.
• Annual offsite — The whole team, once a year, somewhere worth the trip.
• Tech Syncs — Engineering and Product meet in person at least quarterly, worldwide.