Freelancer profile translated to English.
Back to original languageAbout Bachri
Data Analyst & Data Engineer — Python, SQL, ETL
I design automated, reliable, and reproducible data pipelines for data teams, laboratories, and science-oriented companies. My job: transforming manual and time-consuming processes into robust Python workflows — from data ingestion to reporting.
What I work on
Data processing automation
Cleaning, merging, and consolidating Excel/CSV files, automatic report generation, dataset transformation. You'll save hours lost on manual manipulations, with reliable processing.
ETL pipelines & databases
Design of complete pipelines — ingestion, transformation, validation, PostgreSQL storage — in a Dockerized architecture. Your data flows will be centralized, historized, and reproducible.
Dockerization & reproducible environments
Containerizing your Python workflows: simplified deployment, identical execution on any machine, lighter maintenance.
Scientific data analysis
Processing experimental data, statistical analysis, segmentation, and scoring. I work with large and heterogeneous datasets with the rigor of a scientific background.
My method
Understand the business need before writing a single line of code. Prioritize automating what truly wastes time. Deliver documented and easy-to-maintain solutions that you can run without depending on me.
Tech Stack
Python (Pandas, NumPy, Scikit-learn), SQL, PostgreSQL, Docker, Git, R — ETL pipelines, automation, data processing.
Languages
French, English, and Spanish — native level. I work seamlessly on multi-country projects.
English
Native or bilingual
Spanish
Native or bilingual
French
Native or bilingual
Remote only
Primarily works remotely
Experience
- Développeur Workflow & Computer Vision3D Quantitative Analysis Automation — Python / Docker / AIJanuary 2026 - Today (5 months)Toulouse, FranceAutomated 3D image analysis pipeline — Python, Docker, AIDesign of an automated 3D image analysis pipeline: extraction, segmentation, and visualization of spatial data from high-resolution images.- Complete Python workflow to automate object segmentation via AI (Cellpose)- Reproducible pipeline for processing and consolidating quantitative data- Automated feature extraction and structured dataset generation (CSV)- Interactive 3D visualization (Napari) for volumetric data exploration- Structure detection via spatial analysis and clustering (K-Means, SVD)- Complete containerization with Docker — portability and reproducibility- Pipeline optimization for different hardware profiles (high performance / standard / light)- Automated batch processing on large datasetsStack: Python, Docker, Pandas, NumPy, Napari, Cellpose, Scikit-learn, GitResult: modular pipeline capable of processing complex 3D images, automatically segmenting structures, and producing reproducible quantitative analyses.
- Consultant Data & AutomatisationSegmentation and Data Scoring Automation — Python / Machine LearningFebruary 2026 - April 2026 (2 months)Segmentation and scoring pipeline — Python, Machine LearningDesign of a processing and analysis pipeline automating the segmentation of a large dataset and the classification of new profiles, to support decision-making.- Cleaning and structuring of over 13,000 lines of heterogeneous data- Automation of preprocessing: missing values, normalization, standardization- Analysis and scoring workflow with Python and R- Development and comparison of several classification models- Segmentation algorithms to automatically identify coherent groups- Visual analyses to facilitate result interpretation- Reproducible environment (Git, Conda)Stack: Python, Pandas, Scikit-learn, Matplotlib, R, Git, CondaResult: automated pipeline capable of classifying new data, identifying actionable segments, and accelerating decision-making analysis.
- Data Engineer — Pipeline ETL & Orchestration DockerAutomated ETL Pipeline in Containerized Architecture — Python, PostgreSQL, DockerDecember 2025 - January 2026 (1 month)Toulouse, FranceAutomated ETL pipeline design: ingestion, processing, validation, and storage of large data volumes in a reproducible containerized architecture.- Complete Python ETL pipeline to automate the ingestion and processing of structured data- Multi-service architecture with Docker Compose and PostgreSQL- Automation of deployment and environment management- Synchronization and retry mechanisms to ensure reliable inter-service communication- Automated data cleaning, validation, and transformation (Pandas)- Historized SQL schema ensuring traceability of processing- Secure SQL inserts (parameterized queries, transactional management)- Automatically generated quality control metrics and dashboardsStack: Python, PostgreSQL, Docker, Docker Compose, Pandas, SQLAlchemy, psycopg2, Bash, GitResult: modular pipeline capable of automating complex data workflows, centralizing and historizing processing, and accelerating quality control.
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Education
- Master's Degree in BioinformaticsUniversity of Toulouse2027Master's Degree in Bioinformatics
- Bachelor's Degree in Life SciencesUniversity of Toulouse2025Bachelor's Degree in Life Sciences
Certifications
- The Complete SQL Bootcamp: Go from Zero to HeroUDEMY2026