About Olivier
Olivier is a big data engineer skilled in distributed processing, cloud architecture, data visualization, and machine learning. He has been processing terabytes of data as a team player to help hundreds of automotive engineers build your next smart vehicle. With experiences in both R&D centers, retail and banking environments, Olivier knows how to make the most of your data.
English
Fluent
French
Native or bilingual
Remote only
Primarily works remotely
Experience
- Clas OhlsonData EngineerRETAIL (LARGE RETAILERS)June 2020 - Today (5 years and 11 months)• Implemented a production-ready data platform, infrastructure as code, relying on a big data stack for scaling, enabling company-wide data science, machine learning, and data services projects to run.• Created a fully automated ELT system, getting data from more than six various sources. Ran daily, fully automated with tests and alerts, using a mix of Azure services and DBT to keep the cost low.• Deployed and configured an "Airflow-like" orchestration tool (Prefect) to reduce the manual work and ease data pipelines and ML pipeline management.• Created and configured tools and data-related services for data scientists, data analysts, and business workers.• Provided expertise formation and advice to the teams over data engineering concepts, cloud infrastructure, network security, and best practices.
- Continental AutomotiveData EngineerAUTOMOBILESeptember 2017 - March 2020 (2 years and 6 months)Toulouse, France► PREDICTIVE DIAGNOSIS:Improve Continental’s component lifespan thanks to vehicle’s data exploitation:• Design and deployment of an entire scalable data pipeline, from data collection to analysis and interactive visualization• Fully implemented on AWS with Terraform « infrastructure as code »• Anticipation of the increase of data quantity : part of the code is serverless (AWS Lambdas in Go/Python), distributed processing with Spark (Scala) on AWS EMR and queries with Athena (SQL).Results: More than 20 pipelines and terabytes of structured data queried on a daily basis by 100+ automotive engineers.Tools: Amazon Web Services (AWS), AWS IoT, Kinesis, Lambdas, EMR, Spark, AWS Data Pipeline, Athena, EC2, ECS, S3, RDS► MACHINE LEARNING:Multiple machine learning tasks to solve specific automotive issues:• Statistical analysis over large quantity of data (EMR/EC2 with SparkML / SKLearn)• Machine Learning models creation and tuning, feature engineering to improve physical models of engine behaviors and pollutants emissions.Results: 2 patents, many modelsTools: Amazon Web Services (AWS), Jupyter notebooks, Python, SKLearn, Spark ML, Tensorflow, Keras, Pandas, Scipy, Plotly, Bokeh
- CGIData Engineer (ETL/BI)DIGITAL AND ITJanuary 2016 - September 2017 (1 year and 8 months)Toulouse, France► SOCIETE GENERALE• Complex transformation and loading of TB of data• Specifications and expert advises to a team of developers• SQL queries optimization for performance improvementIBM Datastage, Teradata, TPT, Shell Unix, CTRL-M, SAS► THALES• BI and ETL tooling development and monitoring for Thales company• Data analysis and SQL expertise• Oracle database, Oracle BI 10/11g, Informatica Powercenter, Siebel, DAC► PERNOD RICARD• BI and ETL tooling development and monitoring• Data analysis and SQL expertise• Oracle database, Oracle BI 10/11g, Informatica Powercenter
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Education
- Ingénieur en Génie IndustrielENSIACET, Toulouse2015Département Génie industriel avec une forte composante génie des procédés : G. Industriel : Gestion et suivi de projet, gestion des risques, planification, estimation des coûts, méthodes de prévisions, ... G. Procédés : Conception de procédés chimiques industriels, opérations unitaires, maîtrise des logiciels dédiés (ProSim), évaluation économique des Procédés, ...
- Ingénieur en Génie IndustrielENSEEIHT, Toulouse2015Option de troisième année Eco-Energie à l’ENSIACET et à l’ENSEEIHT (Toulouse, 31) : - Conception de systèmes énergétiques efficaces et durables. - Pluridisciplinarité électricité et procédés.