VIE - Data Engineer (H/F) - New York

Hermès · French: C1 · Commerce · Fashion Design · 4 days ago

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Salary

≈ $119k – $259k per year (Estimated)

Location

In office (New York)

Employment

Internship

Confirmed on the employer's own hiring board on Nov 2, 2026. First seen by Alion on Sep 30, 2026.

Overview

Company

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Hermès

Hermès is a French luxury goods house headquartered in Paris that designs, makes and sells leather goods, silk, ready-to-wear, jewellery, watches, home objects and fragrance and beauty products. Founded in 1837 by Thierry Hermès as a harness workshop, it is still controlled by the founding family, is listed on Euronext Paris and runs boutiques worldwide, with major workshops and offices in Pantin and the wider Paris region. Its openings cover boutique sales associates, product managers, communications and media roles, client development, craft and production staff and its Generation H internships.

hermes.com · HQ: Paris, France · Fashion Design · Commerce · Bags, Luggage & Leather Goods · Luxury Goods & Designer Brands · Luxury Retail · 5000+ employees · Est. 1837

Tech stack of Hermès

Rejoignez Hermès, une maison de luxe française, en tant que Data Engineer à New York. Vous serez l'un des premiers ingénieurs à poser les fondations techniques de notre nouvelle Data Platform cloud. Vous travaillerez en étroite collaboration avec un Data Analyst et serez impliqué dans un programme de migration de données à fort enjeu. Ce poste nécessite une rigueur et une structuration solides, ainsi qu'une aisance en anglais professionnel.

Missions

Concevoir et développer les pipelines de données depuis les sources locales US vers Snowflake, en assurant la qualité, la fiabilité et la traçabilité des flux de données.
Modéliser les datamarts métier en conformité avec les data models cibles définis par la direction, et structurer les couches de transformation selon les standards dbt.
Cartographier les sources de données existantes sur les plateformes locales et établir leur correspondance avec les nouveaux modèles, en identifiant et traitant les écarts de définition ou de grain entre systèmes sources et cibles.

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