Data Engineer, Americas

Company Description

LVMH Beauty activities benefit from exceptional dynamism that relies on both the longevity and development of key lines, and on the boldness of new creations.

All are driven by the same values: a quest for excellence, creativity, innovation, and perfect mastery of their image.

The brands cultivate what makes them unique and is guaranteed to make them stand out in a highly competitive global market. The success of the Beauty division depends on finding the right balance between major historic Houses such as Parfums Christian Dior, Parfums Givenchy, Acqua di Parma, Guerlain, and newer brands with strong potential like Kenzo Parfums, Fresh, and Make Up For Ever.

LVMH Beauty invites you today to join its North America teams.

LVMH Beauty is part of the LVMH Group.

The position is with LVMH Beauty Tech within the regional Americas team managing the Data platform and applications 

LVMH Beauty Tech regional team is split between the North America LVMH Beauty Shared Service Center, the Mexico LVMH Beauty Shared Service Center and its San Francisco offices, servicing all major LVMH Beauty Brands for the region: Acqua di Parma, Benefit Cosmetics, Fresh, Guerlain, Kendo, Kenzo, Maison Francis Kurkdjian, MAKE UP FOR EVER, Parfums Christian Dior, Parfums Givenchy and Stella.

Job Description

Within the Data team, the role will be to participate in the design and the development of the Data platform (based on Google Cloud Platform and Dataiku).

Provide business teams with standardized, reliable, up-to-date and actionable data.

Work in close collaboration with other members of the WW Data team (Data tech lead, data engineers, data scientists) and other Beauty Tech teams (especially CRM, retail and e-commerce) to build a reliable, scalable and secure data platform.

• Work on the entire data production chain by implementing data ingestion pipelines (from multiple sources and in different formats), storage, transformation ... then their provision: datamarts, reports, datasets to feed models scoring (data science), API, ... mainly using Dataiku and Google Big query
• Ensure that the integration pipelines are designed in a manner consistent with the overall data framework in collaboration with the data tech lead and according to best practices.
• Be part of a continuous improvement approach by optimizing and reusing existing assets.
• Take part in data integration processing aspects of data quality controls, monitoring, alerting and technical documentation, as well as data management (data models and mapping, data documentation, repositories, description of the transformations applied, etc.)
• Acquire a good understanding and analysis of business challenges and be able to translate the needs into the design of concrete technical solutions and gradually extend the functionalities and scope of our data platform based on Google cloud platform and Dataiku.
• Provide reliable estimates of workloads and planning according to the level of complexity and other activities to allow a good coordination of the activities of the team.
• Perform unit development tests and support business users in their tests before final validation.
• Contribute to the design and management of the data model, as well as to the orientations in terms of the architecture of our data platform (repositories, APIs, etc.)
• Set up pipeline monitoring and monitoring of the data platform and APIs (from a functional point of view and data quality)
• Analyze incidents, points of weakness and support requests related to the use of the data platform or APIs
• Provide timely and accurate support to the business teams on issues
• Propose improvements to optimize the data platform (optimization of existing processes, data restructuring, factorization, etc.)

Reports to the Data Domain Director based in East Brunswick, NJ

Qualifications

Key competencies:

• Mastery of the data stack components in Google Cloud Platform (certifications appreciated) including but not limited to: Google Big Query (nested fields, partitioning, merge SQL, authorized views, RLS, …), Cloud storage, Cloud functions, Cloud composer, Google Firestore, Google data catalog.
• Proficiency of Dataiku (on Google big query): development of dataiku flows, implementation of scenarios, scheduling, management of versioning, releases into production, administration etc.
• Mastery of complex SQL queries
• Good knowledge of Python is a plus
• Development practices with data exchange architectures: webservice, API, streaming. Salesforce MuleSoft a plus.
• Development in an agile team and the tools used in CI / CD (Azure devops, Jira, Confluence)
• Knowledge of Microsoft Power BI, data catalog tool, data quality, data management
• Knowledge of Terraform a