Build, deploy and maintain data infrastructure of any scale using DevSecOps/DataOps methodologies.Automate your data infrastructure while making them compliant by design.Large-scale migration, data lake/mesh design and implementation.

- Architected of the whole data platform from scratch- Built and deployed the complete platform with Terraform on AWS and kubernetes- Integration of all data sources: Hubspot, Amplitude, Sendgrid, Chargebee, etc.- Designed and implemented a strategy to fix data reconciliation issue, including set up and integration of a kafka cluster, data API and streaming pipelines.

Built a decentralized streaming platform on AWS and blockchain tech.Designed and operated a fully decentralised data marketplace for creators to exchange dynamic datasets freely.

Data consulting company with 15 teammates and specialised in data/analytics infrastructure at scale for DAX-listed companies such as Zalando, Rocket Internet, ProSiebenSat.1.ETLs (airflow, Spark), process mining (Celonis), data warehousing (Exasol, Redshift), cloud infrastructure (AWS and GCP), data governance (Collibra), data quality, etc.

• Mission 1 / developed first analytical pipelines for operational reporting (3 months): brought visibility on internal processes related to data processing of merchant articles, which participated in bringing down article processing time by 95% saving hundreds of millions and helped scale the business unit.• Mission 2 / migrated infrastructure to modern stack (3 months): migrated the old stack relying on Pentaho Kettle and Oracle, to a more modern Apache Airflow and Spark, using Nakadi (Apache Kafka) as data sources• Mission 3 / built real-time Data Warehouse for Process Analytics (1 year): after identifying the business requirements supporting the business cases for a data warehouse, conceived and build a near real-time data warehouse for the purpose of supporting analysis of merchant article lifecycle from onboarding to end of life. The data warehouse currently enables 15 teams and more than 50 users for reporting, data engineering and ML. Notable technical points: 2 Pb of historical data processed in Spark; achieved a lag of 30min to reality in production using Spark’s structured streaming with the Scala API. • Mission 4 / developed new Data Team (6 months): transformed and standardized all data pipelines to embrace the DataOps methodologies in order to support the organization in its transition from a BI team to a new team solely staffed with software engineers. Investment into monitoring, automated data testing and QA. • Mission 5 / Process Mining at scale (6 months): used the data warehouse as a platform to enable process mining at scale of critical processes via the full enablement of business analysts by removing entirely the need for report developer. The goal is to provide a holistic and near real-time analytical platform that allows any business user to data mine and use AI to control the different processes of the business unit: article workflows, order management and logistics.

• Central Data Warehouse: Designed and built a central data warehouse for the entire portfolio aiming at creating a unique data portal for the investment managers and supporting the central reporting services of the holding• Central Reporting Platform: Developed standardized reports for all assets as well as benchmarking reports. The database and the reports act as a single source of truth for the communication within the investment management for 25+ confidential end users• Data Engineering: Built ETL bi-directional workflows to pull and push data between the holding data warehouse and the data warehouses of the assets with Apache Airflow. This Redshift cluster is fed by data from 25+ data sources, including many data warehouses, accounting data, competitive intelligence APIs web analytics, TV data, etc. • Business Logic: Standardized at maximum KPIs across all ventures, as well as data, attribution models and cost allocation models to enable cross-venture comparison and analyses. This standardized data fed our model for TV spot budgeting and distribution among the portfolio. • Excellence Center: Organized global workshops, set-up a global Slack to connect all data department of all assets. Provided technical guidance and data engineering expertise for all assets

• Management: Set up and led a team of 4 analysts while re-aligning the department roadmap with the company strategy. • Strategy: Assisted MDs and board members on continuous business planning and forecasting, recommendations, special projects. Redefined the KPI tree and shifted the focused to the real business drivers.• Technology: Redefinition of the BI environment. Migration to PostgreSQL, UNIX servers, implementation of Tableau. Migrated all ETL operations from third-party tools (Talend and SSIS) to scripts (Python, SQL, SQL), allowing a lag lower than 5min between the datawarehouse and the 7 different Magento back-ends of the shops.• Data Science: Statistical analysis of customer surveys (>100Gb of data). Other project example: spatial interpolation using geographical densities to automatically determine under-exploited regions for targeted local marketing.• Acting Head of CRM (on top, 6 months): Designed a customer-oriented CRM project for the company: architecture and implementation of a customer science-enabled ecosystem with a deep integration into the BI infrastructure.

• Datawarehousing: Worked on all the phases of the data warehouse development lifecycle, from gathering requirements to data modelling, implementation and automation of all ETL processes using SSIS.• Reporting & Ad-Hoc analyses: Developed the first company reporting environment on SQL Server (SSRS and SSAS) and PowerPivot. In charge of the reporting environment and all data streams for internal operational use.• Communication: In charge of incoming requests to BI. Weekly calls with the C-level and MDs from 5 countries.• Data valuation: In charge of all data due diligence during the sale of 3 country entities. Assisted the negotiator during the sales meetings for all data preparation and data valuation.• Applied operation research: developed a fully automated targeting tool using combinatorial optimization for operational purpose that led to an algorithm-backed process and reduced by 95% the time needed to perform the product allocation, increasing customer satisfaction and extending customized services to the customers and the suppliers.