Adin Hrelja adindata

• Cross-functional role within the Drive Data Hub team • Reverse engineering of the data layer on GCP • Cost optimization and FinOps dashboards to monitor BigQuery consumption in real time • Support for data teams on optimization, scalability, and governance • Implementation of GCP, GitLab, and BigQuery best practices • Setup of GitLab CI/CD pipelines for Dataform • Deployment of Airflow for the application layer • Reduction of GCP costs through the optimization of 350+ SQL scripts and 120+ Python scripts • Significant improvement of data processing performance • Preparation for data monetization via Looker for suppliers (product insights) • Design of a scalable and production-ready BI architecture • Audit and rationalization of existing BigQuery queries (performance + cost) • Implementation of FinOps best practices (monitoring, job-level cost allocation, efficient partitioning) • Animation of workshops to upskill teams on BigQuery and Airflow usage • Interface between central FinOps and business teams to communicate standards and cost trends • Development of Looker dashboards for supplier analytics • Automation and redesign of Python pipelines using Dataform and Terraform • Full technical documentation produced in English in an international context • Architecture audit and delivery of a new BI reference architecture • Integration of Dataform for data modeling, tests, version control, and CI/CD • Deployment of Cloud Composer (Airflow) on Carrefour’s private subnet • Development of a Python service for Qlik API interactions • Migration of a legacy "manquants d’entrepôts" project into Dataform (tests, documentation, CI/CD) • Training and enablement of BI teams on modern tooling and workflows • Operational support and troubleshooting to secure production pipelines • Significant reduction in the number of queries executed daily • Noticeable improvement in ETL/ELT performance (reduced runtime) • Delivery of the first monetizable dashboards for internal testing • Adoption by BI teams of a modern, maintainable architecture facilitating production releases • Implementation of a BigQuery log export pipeline (Billing Export + Audit Logs) with real-time Looker Studio monitoring: • Cost by job_id, dataset, user, location, query type • Metrics analysis: bytes processed, bytes billed, slots usage, execution time • Creation of a partitioned FinOps BigQuery datamodel • Real-time Looker Studio dashboard with estimated cost per pipeline, trend analysis, anomaly detection • Automated detection of problematic SQL patterns using Python + BigQuery Technical environment : GCP, bigquery, python, sql, gitlab, terraform, jenkins, vault, Airflow

• Migration of the data stack to GCP • Implementation of the DevOps layer and GCP best practices • Data preparation for Data Science and BI teams (MicroStrategy) • Coordination between technical and functional teams • Run and maintenance of the legacy stack (SSIS and SQL Server) • On-call duty two weeks per month • Data pipeline automation and integration with G4A Technical environment : GCP, bigquery, python, sql, bitbucket, terraform, SSIS, sql server

• Implementation of GCP best practices (GitLab, Terraform, CI/CD, data warehouse, data marts) • Cross-functional role as data lead within the Chloé Data Hub (team of 6 consultants) • Data project manager for CRM, Sustainability, and Merchandising teams • Migration of CRM data pipelines (from Dataiku to GCP) • Automation of data pipelines for Supply, Merchandising, CRM, and Sustainability teams • Weekly Business–Data reporting updates • Retail data quality ensured at 99.98% with Bex • Development of data products such as customer segmentation historization • Creation of Merchandising dashboards (50 pages, 3 dashboards: Weekly, Monthly, Yearly by product hierarchy), including a Monday morning newsletter for Comex members and Richemont top management (Fashion & Accessories division) • Data corrections for seasonality calculations for the Merchandising team • Development of dashboards to track Chloé’s top clients in real time (Looker Studio) • Support for data consultants on Sustainability topics to address business needs • Business trips to Meyrin (every 3 months) to collaborate with GroupTech and Chloé Data teams • Automation of stock delivery reports for wholesalers (Europe, Middle East) Technical environment : GCP, bigquery, python, sql, gitlab, terraform, dbt, Terraform Enterprise

Cloud Engineer GCP at Bel : • Creation of a data warehouse and a data mart on BigQuery • Implementation of a data pipeline on GCP • Junior Data Engineer within a five-person team • Implementation of data projects using Agile methodology Technical environment : GCP, bigquery, python, sql, gitlab, terraform, jenkins, docker

Mission at Saint Laurent : • Implementation of GCP best practices • Development of a data warehouse and a data mart on BigQuery • Data transformation of the Saint Laurent newsletter using Cloud Functions and BigQuery • Implementation of an ELT pipeline on GCP • Monitoring of GCP costs and BigQuery usage with Looker Studio Technical environment : GCP, bigquery, python, sql, gitlab, terraform, docker