채용
Benefits & Perks
•Learning Budget
•Mental Health
•Learning
•Mental Health
Required Skills
Databricks
Apache Spark
Delta Lake
Microsoft Fabric
Azure
Data engineering
Cloud platforms
Data governance
Security
DevOps
CI/CD
SQL
Your role @ CHANEL
The Manager, Data Platform Engineering leads and evolves the cloud data platform for NA and SEAA regions. Ensures the data platform readiness for power reporting, dashboard and advance analytics, support client activation journeys and digital products across divisions and markets in NA and SEAA regions.
Is the strategic builder of governed, scalable, and cost‑efficient data platform capabilities — accelerating time‑to‑onboard new sources, improving reliability and performance, and ensuring security, privacy, and compliance — while coaching and growing a high‑performing engineering culture.
The impact you can create at CHANEL
- Own, implement and governs the data platform architecture and patterns (e.g., Lakehouse architecture)
- Build and operate secure, reliable data pipelines (batch and streaming) and data contracts for source onboarding, CDC, and interoperability across teams
- Implement governance and data quality at scale: metadata and lineage, schema evolution, automated testing, master/reference data, etc.
- Manage small technical teams made of internal and external resources to deliver agreed outcomes for Divisions and Markets
- Leads involvement on all related governance forums representing the data platform
- Partner closely with Data Enablement & Activation teams to deliver performant, privacy ready datasets, and APIs for CRM, journeys, segmentation, and models
- Embed security and privacy by design: fine grained access controls, encryption, masking, consent, residency/retention, and audit readiness
- Drive standards and reusability: shared technology components, templates, documentation, and communities of practice to scale best practices across platform users and developers
- Collaborate, and manage external providers (e.g., Databricks, Microsoft), to develop roadmaps, feature adoption, licensing, etc. and evaluating new capabilities through Po Cs
You are energized by…
- Turning complex data problems into simple, elegant platform solutions that users and developers love to use
- Owning reliability — Solving bottlenecks, making systems resilient and hard to break
- Coaching engineers, and co creating, with analytics teams in divisions and markets to unlock high impact use cases
- Measuring improvements (eg. faster onboarding, better performance, lower cost) and iterating rapidly
- Building with care: secure by design, privacy respectful, and audit ready from day one
What you can bring to the team…
- Bachelor’s or Master’s degree in Business, Computer Science, Information Systems, or related field
- 7+ years of experience in data management, CRM, analytics, strategy, or program management
- Deep, hands on, real-world expertise creating and managing Databricks workloads (Spark, Delta Lake, Unity Catalog, Jobs/Workflows, SQL) at scale in production. Ideally a Databricks certified professional.
- Bring strong working knowledge in Microsoft Fabric (One Lake, Lakehouse, Data Engineering, Data Factory, Warehouse) and any Azure related data services.
- Proven experience designing and running modern Lakehouse architectures in the cloud, batch/streaming pipelines, and CDC integrations
- Understands DevOps discipline applied to modern cloud data platforms: CI/CD (Azure DevOps), automated testing, observability, incident/change management, and environment configuration
- Applies a governance first mindset: metadata, lineage, data quality, access controls, and compliance with consent, residency, and retention requirements
- Drives security by design: row/column level controls, secrets management, encryption, and least privilege IAM
- Posses a Fin Ops acumen: cost efficienty , auto scaling, right sizing, and unit economics for pipelines and workloads
- Clear communicator and collaborative partner — working across divisions/markets, analytics, activation teams, and external suppliers
What you will learn / What CHANEL can offer you…
- The opportunity to shape regional data technology platforms, making a lasting impact on CHANEL’s digital future.
- Growing with the data domain and learning latest innovations in data, client activations, and AI applications
- A collaborative culture that values integrity, humility, and open-mindedness, where your ideas and contributions are truly welcomed.
- Growth through cross-functional engagement and architectural leadership, empowering you to expand your skills and influence.
- A front-row seat at the forefront of digital transformation and enterprise modernization, where innovation and creativity are celebrated.
At Chanel, we are you focused on creating an inclusive culture that nurtures personal growth, contributing to collective progress. We believe the uniqueness of each individual increases the diversity, complementarity and effectiveness of our teams. We strongly encourage your application, as we value the perspective, experience and potential you could bring to Chanel.
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About Chanel

Chanel
PublicFrench luxury fashion house known for haute couture, ready-to-wear clothing, handbags, perfumes, and cosmetics. Founded by Gabrielle 'Coco' Chanel, the company is recognized for timeless designs including the Chanel suit and Chanel No. 5 perfume.
10,001+
Employees
Paris
Headquarters
Reviews
3.3
9 reviews
Work Life Balance
2.5
Compensation
2.8
Culture
2.3
Career
2.7
Management
2.2
35%
Recommend to a Friend
Pros
Good discounts and perks
Prestige of working for luxury fashion brand
Quality training and learning opportunities
Cons
Poor management and leadership
Low pay and compensation issues
Toxic workplace culture and drama
Salary Ranges
56 data points
L2
L3
L4
L5
L6
Mid/L4
Senior/L5
VP
L2 · Financial Analyst L2
0 reports
$108,449
total / year
Base
$43,380
Stock
$54,225
Bonus
$10,845
$75,914
$140,984
Interview Experience
2 interviews
Difficulty
3.0
/ 5
Duration
14-28 weeks
Offer Rate
50%
Experience
Positive 50%
Neutral 0%
Negative 50%
Interview Process
1
First Interview
2
Second Interview
3
Design Task
4
Week-long Trial
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