Karim Zhan

– As a Data/Analytics Engineer at Saint Laurent, I sit at the intersection of technical expertise and business understanding — working in direct collaboration with Supply Chain and Retail & Sales teams to turn their needs into production-ready Data Products on GCP. – Our source system is SAP, which requires a deep understanding of its data model to extract clean, meaningful and business-ready outputs. From there, I own the full lifecycle of each Data Product: scoping with stakeholders, modeling in BigQuery, testing, CI/CD deployment and end-user adoption. All Data Products are built within a Data Mesh architecture, treated as standalone assets — governed, documented, and exposed either through PowerBI / ToucanToco dashboards or as self-service analytics. – Supply Chain: – Availability Rate — product availability monitoring to proactively detect stockout risks – Launch Planning — inventory readiness tracking aligned with collection go-to-market timelines – Stock Visualization — stock level KPIs across products, regions and channels – Weeks of Coverage — forward-looking indicator to anticipate replenishment needs – Retail & Sales: – Weekly/Daily Sales Overview — daily & weekly store performance KPIs – Weekly Retail Performance — KPI tracking across regions and channels – Wholesale Sellout — sell-out visibility from wholesale partners – Shopping Insider — digital performance consolidation from Google Ads & Google Merchant Center – Technical scope: – Source system: SAP → ingested and modeled on GCP / BigQuery – ELT pipelines with dbt (SQL, Jinja, macros) CI/CD via GitHub Actions – GCP infrastructure as code with Terraform – BigQuery optimization: partitioning, clustering, sharding – Agile / Scrum on Jira – Stack: BigQuery dbt Terraform Python SQL Git SAP PowerBI

– As a Data Engineer at L'Oréal, I contributed to a large-scale SAP-to-GCP migration, transitioning legacy data infrastructure toward fully managed, serverless GCP services — improving scalability and reducing operational costs. – Working closely with business stakeholders across Finance, Supply Chain and Customer domains, I translated business requirements into technical specifications and delivered end-to-end data solutions, from pipeline design to analytical reporting. – Key contributions: – SAP to GCP migration — implemented serverless solutions to replace legacy SAP data flows, improving scalability and cutting operational overhead – Data Warehouse design — built multiple data warehouses using BigQuery, Cloud Workflow and Cloud Scheduler – REST API pipelines — designed and implemented multiple data pipelines integrating REST APIs on GCP – CI/CD — set up deployment pipelines using GitHub, Cloud Build and Terraform – Analytical reporting — developed business reports in close collaboration with domain partners, ensuring alignment with Finance, Supply Chain and Customer data needs – Technical scope: – GCP services: BigQuery Cloud Storage Cloud Pub/Sub Cloud Tasks – Cloud Workflow Cloud Scheduler Firestore Compute Engine. IAM – Infrastructure as code: Terraform – Languages: Python SQL (on Visual Studio Code, GCP VMs) – Agile / Scrum on ServiceNow / Jira

– As a Data Engineer embedded within L'Oréal China's Consumer domain, I worked on complex multichannel data challenges specific to the Chinese market across offline stores and major online platforms including Douyin and Alibaba. – Fluent in both Mandarin and English, I collaborated daily with cross-functional teams and Consumer domain analysts to ensure data solutions were tightly aligned with business objectives. – Key contributions: – Gift article mapping — designed and implemented data procedures to match 10,000+ gift items to their respective products across offline and online channels (Douyin, Alibaba), enabling accurate gift policy analysis and purchase behavior tracking – Consumer analytics — integrated Consumer domain analyst requirements into scalable data solutions supporting strategic business initiatives – Data quality — ensured data integrity through rigorous testing across GCP and Databricks environments – Cross-cultural collaboration — bridged technical and business teams effectively across languages and time zones – Stack: BigQuery Databricks GCP Python SQL

– Département des systèmes informatique

– Département installation