Hello !
I am an Engineer specialized in developing multithreaded applications and distributed systems (Big Data).
These two paradigms, while distinct, share a common philosophy: maximizing computational power through parallelization !
From constructing ETLs with Scala and Python to developing multithreaded applications with Akka (Scala) and Tokio (Rust), I leverage frameworks tailored to each challenge. My AWS certification and Databricks training further strengthen my expertise in scalable cloud solutions and Big Data processing.
I am able to work with you on:
o Conception of diverse ETL, databases, multithreaded applications.
o Realisation, industrialization of data processing pipelines.
o The collect and exploration of your data.
o Automatization of Data Ops : git, CI/CD
o Contribute to the constant improvement of your team's agility
o The development of Spark applications for the processing of big data at scale
I am also skilled with : Agile methodology, AWS, Kubernetes, Databricks, TDD.
Experience: 3 years
Yearly salary: $60,000
Hourly rate: $290
Nationality: 🇫🇷 France
Residency: 🇫🇷 France
Experience
Big Data Mission in Market Finance
NATIXIS corporate & investment banking 2024 - 2024
Development of Spark/Scala Workflows: Automated reserve calculations across various financial assets, optimized for high data volumes. Data Integration from Multiple Sources: Integrated data from Hive, IQ, and ASE for rapid access by financial analysts and data scientists. CI/CD Pipelines: CI/CD pipelines with Jenkins to automate and ensure reliable deployments. BDD Testing with Cucumber: Implemented comprehensive functional testing for calculation workflows. Job Scheduling on Control-M: Scheduled and managed jobs to ensure efficient processing. Hive Scripting for Intermediate Tables: Developed Hive scripts to create intermediate tables and optimize data access.
Arbitrage Bot (MEV)
CyptoLayout 2024 - 2024
Scalable Cross-EVM Solution: Millisecond-level arbitrage detection with an asynchronous, multithreaded architecture and secure data sharing (thread-safe). High-Performance Token Pricing Algorithm: Recursive algorithm enabling token price estimation with detection at sub-millisecond precision. Advanced Tracing and Real-Time Monitoring: Arbitrage opportunity tracking with Elastic Stack (Filebeats, Kibana). Robust Unit Testing and Generic HTTP Solution: Comprehensive unit testing and a generic solution for HTTP request/response management. Deployment Scripting and Binary Versioning. BSC Node Configuration: Configured BSC node with Geth, currently testing with Reth for enhanced performance.
Big Data Mission on a CRM Tool (Data Flow Migration)
BNP Parisbas 2022 - 2022
Code Coverage (90%) in Scala/Maven. Data ingestion package maintenance (Hive to Cassandra). Adaptation of Spark processing. Implementation of Oozie scheduler. Statistical analysis in Cassandra using Spark-shell (Data Integrity).
Big Data Developer Mission for Fraud Prevention
BNP Parisbas 2022 - 2023
High-Performance API Development for Real-Time Fraud Detection: Developed on a multithreaded, scalable API with Play/Akka (actor model) capable of processing millions of records daily, achieving millisecond-level fraud detection. Continuous Data Stream Integration: Integrated Kafka for real-time data flow within the API, enhancing responsiveness and data handling. Large-Scale Data Processing: Developed and optimized Spark/Scala jobs for efficient processing of high data volumes. Data Modeling and Query Optimization: Created CQL (Cassandra Query Language) models and queries for efficient storage and rapid retrieval (Design by query). CI/CD Pipeline with Real-Time Monitoring: Set up a Jenkins CI/CD pipeline with real-time monitoring and alerting via Splunk and Dynatrace. Comprehensive Testing: Implemented unit and integration tests to ensure 90% code coverage.
Prove of Concept
Aubay 2021 - 2022
Deployment of Auto Machine Learning Platform on AWS Instance. Kubernetes cluster virtualization on Docker using kind. Deployment of Kubeflow on the cluster (auto-ML platform). External access to pods: user interface, Kibana. Implementation of machine learning pipelines coded in Python. ELK index mapping and prediction visualization on Kibana.
Data scientist (Internship)
Aubay 2021 - 2021
Computer Vision Algorithm: Object detection, segmentation, and distance estimation of objects in a monocular image. Use of Pre-trained Models: maskRCNN, Yolo, detectron2, OpenCV. Geometric Method: Inverse Perspective Mapping (IPM). Development: Application with ReactJS (frontend) and Flask (backend).
Skills