vibe - Director of Data Engineering
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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 • Hands-on experience with clean room technologies (Snowflake Data Clean Rooms, AWS Clean Rooms, LiveRamp Safe Haven, or similar) • Familiarity with MLOps tooling — feature stores (Feast, Tecton), model serving (SageMaker, Ray Serve), orchestration (Airflow, Dagster) • Background in streaming TV, connected TV, or programmatic advertising infrastructure • Experience leading a technical team through a SOC2 certification process • At Vibe, we run on three values: Impact, Ambition, and Urgency. We tie every goal to a real business outcome, think 10x rather than settling for good enough, and move daily rather than waiting for perfect conditions. • You'll thrive here if you own outcomes without needing direction, default to action, and hold yourself to results — not effort. • This is a high-performance environment with high-performance rewards: you'll work alongside people who push you, with real ownership over hard problems in a market that's moving fast. • If that sounds like the best version of your career, we want to meet you.
Responsibilities
• Architect the data platform • 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 • Build the ML infrastructure • 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 • Lead governance without slowing people down • 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 • Own infrastructure economics • 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
Benefits
• Chez Vibe, nous voulons que vous construisiez quelque chose, possédiez quelque chose, et progressiez vite. Voici comment nous le prouvons : • 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. • OUR INTERVIEW PROCESS • We respect your time. Here's exactly what to expect — no surprises, no ghosting. • 1. Recruiter screen — 30 min. We'll share context; you'll share yours. • 2. Manager interview — Meet the person you'd work with directly. • 3. Take-home assignment + live debrief — A real problem, not a trick. We'll work through it together. • 4. Calibration interview — A senior leader joins to ensure we're holding a consistent, high bar. • 5. Offer — Fast. We don't let good decisions sit. • 6. Reference checks — Two calls, handled with discretion. • EQUAL OPPORTUNITY
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