Description

Data Engineer – AWS | MLOps | Cloud-Native Pipelines

I help businesses (from startups to large enterprises) unlock the value of their data by designing and implementing scalable, secure, and production-ready data architectures.

With strong experience across the AWS ecosystem and a deep understanding of both engineering and analytics needs, I build end-to-end data solutions: from ingestion and transformation to deployment and monitoring of ML models. My goal? Deliver fast, reliable, and insightful data systems that empower smarter decisions.

Tech stack & skills:

• Cloud & DevOps: AWS (Glue, Lambda, S3, Athena, Redshift, DynamoDB, CloudFormation, CodePipeline, Sagemaker)

• Programming: Python, R, Java, C/C++

• Data: ETL, SQL/NoSQL, PostgreSQL, MySQL, Data Lakes, Warehouses

• MLOps & CI/CD: Terraform, CloudFormation, GitLab CI/CD, Docker, API integration

• Data quality, monitoring, drift detection, KPI tracking

Industry field of expertise

Languages

French
Native or bilingual
English
Native or bilingual

Workplace preferences

Remote only

Primarily works remotely

Devoteam A Cloud
Data Engineer @ Veolia Water Technologies
February 2024 - Today (2 years and 4 months)
Paris, France
Within the Datalake team, I designed and optimized several ETL pipelines on AWS architectures leveraging services such as AWS Kinesis, Glue Jobs, Lambda, SNS/SQS (fan-out pattern), LakeFormation, Step Functions, S3, and DynamoDB to process data in JSON, CSV, and Parquet formats.

A major project in 2024, Daily Aggregations, involved redesigning a critical part of the Datalake, considered the source of truth. Across nine distinct architectures, I managed data ingestion, transformation, and aggregation (15-minute/hourly/daily) using Apache Spark, storing the results in partitioned tables within Glue Data Catalog and DynamoDB. These processes ran daily through various Step Functions and primarily fed our cold storage layer.

As the DevOps referent, I was responsible for managing the development environment during Merge Requests on GitLab CI and actively contributed to deploying improvements using Terraform, CloudFormation, Makefile, Docker, and shell scripts integrated into GitLab CI.
Fortuneo
Machine Learning Engineer
September 2022 - September 2023 (1 year)
Paris, France
(Data Science / Machine Learning Engineering)
Development of Machine Learning models for propensity scoring and deployment (i.e., models to evaluate customer interest in various products/actions such as bank mobility, American Express cards, or savings accounts):

Dataset creation from multiple sources (Data Warehouse, Data Mart, Open Data)
PCA, Clustering
R, Python, Random Forest, XGBoost, LGBM
Descriptive & inferential statistics, KPI analysis, Data Quality (drift detection)
KPI evaluation (Lift curve, Precision/Recall)

Pipeline & Production Deployment (Data Engineering), Data Management & MLOps (AWS Cloud environment):

ETL processes, Data organization, Machine Learning pipelines
SQL, NoSQL (MongoDB), APIs, Unit testing
AWS Lambda, AWS Athena, AWS Glue, AWS S3, AWS CloudFormation, AWS
DynamoDB, AWS CodePipeline, AWS SageMaker Studio
Python, Hadoop HDFS, Hive, GitLab, Data Warehouse, Data Mart
CI/CD deployment on AWS (via CodePipeline)
ALTEN
Data Scientist
November 2021 - April 2022 (5 months)
France
Several Deep Learning projects for the development of a data exploitation platform. During this internship, I was able to strengthen my skills in data visualization and preprocessing, as well as in algorithm modeling, using the Python language and specifically the TensorFlow, Keras, Pandas, NumPy, Matplotlib, Seaborn, and OpenCV libraries. Among these projects were:

COVID-19 detection from lung scans
Classification of heartbeat signals
Face mask detection
Bank fraud detection