AI role · Open offer

Senior Data Engineer — AI feature platforms

Snowflake

Full-time
Senior
AI data platform
Remote
Remote · United States / Europe
Remote, United States
USD 155k–215k + equity

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills3 tools

At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Remote
Location: Remote · United States / Europe
Compensation: USD 155k–215k + equity
Experience level: Senior
Team: AI data platform
Benefits & perks

About this role

What the team is looking for and how this position fits.

Snowflake is hiring a Senior Senior Data Engineer — AI feature platforms on the AI data platform team (Remote · Remote, United States). This is a hands-on role in ML platform & MLOps—not a generic “AI enthusiast” post.

You will own training, deployment, observability, and cost control end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Snowflake alimente les plateformes data et AI pour les entreprises.

Senior Data Engineer — AI features: embeddings, feature pipelines, préparation data pour LLMs.

Missions

Construire pipelines embeddings / features à grande échelle.
Garantir qualité, lineage et coût des données pour l'IA.

Stack & environment

Python, SQL, Spark, Airflow, Snowflake.

What we look for

Data engineering for ML/AI; remote US/EU.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.
Machine Learning

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

SnowflakeTool
Regular use of Snowflake in the ML platform & MLOps stack—configuration, debugging, and team conventions.
SparkTool
Distributed feature generation and batch scoring on large tabular or event datasets.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Feature store, embeddings pipelines, or LLM data prep at scale.
Several of these show up in recent shipped work—not only on your CV: Python, SQL, Machine Learning, MLOps, and Snowflake.
You have owned training, deployment, observability, and cost control in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You communicate clearly in writing, document decisions, and can overlap with the team’s core hours for pairing and incident response.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Feature store, embeddings pipelines, or LLM data prep at scale.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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