Freelancer profile translated to English.

Description

Data Engineer specialized in Azure Databricks, Spark, and Airflow | Robust and scalable data pipelines

Looking for a Data Engineer capable of designing and industrializing your data pipelines? I can help you structure your data infrastructure, from collection to visualization.

My technical expertise:

- **Azure Databricks & Spark**: Big data processing, Delta Lake, performance optimization

- **Apache Airflow**: Workflow orchestration, ETL pipeline automation

- **Data Warehousing**: Data Vault architecture design, business Data Mart creation (marketing, finance)

- **Azure Cloud**: Data Factory, Synapse Analytics, Data Lake, Blob Storage

- **Modern Data Stack**: DBT, Airbyte for modern and maintainable pipelines

- **Visualization**: Power BI, interactive dashboards for decision-making

What I bring to your projects:

- Reduction of manual tasks through workflow automation

- Improvement of data quality and reliability

- Dashboards providing real-time visibility for your business teams

- Support on DataOps best practices and change management

My background:

Currently a Data Engineer at UGC, I lead projects in anomaly analysis, pipeline orchestration, and Data Warehousing on Azure Databricks. Previously at Equans, I designed database architectures, developed Python POCs, and supported teams in adopting data solutions.

I am also a Data Engineer mentor on OpenClassrooms.

Availability:Remote or hybrid missions (Paris and 50km). Open to various mission formats.

Let's discuss your project!

Industry field of expertise

Languages

French
Native or bilingual
English
Fluent

Workplace preferences

Can work on-site

Paris (up to 50km)

OpenClassrooms
Data Engineer Mentor
EDUCATION AND E-LEARNING
September 2025 - Today (11 months)
Paris, France
Mentoring students in the Data Engineer program (bac+5 level diploma). Weekly support on 14 professional projects: ETL pipelines, SQL and NoSQL databases (MongoDB), cloud modeling, workflow orchestration (Airflow), RAG systems with LangChain. Python code review, project validation, and transmission of best practices (data architecture, code quality, technical documentation).
Python MongoDB SQL Data Engineer Mentor
Groupe UGC
Data Engineer
July 2023 - Today (3 years and 1 month)
Orchestration and Anomaly Detection: Full management of an anomaly analysis project (data collection via API, orchestration with Apache Airflow, visualization on Power BI). Automation and Performance: Implementation of pipelines and workflows (Apache Airflow), significantly reducing manual tasks and improving data reliability. Dashboards and Reporting: Creation of dynamic dashboards for management, providing near real-time visibility and facilitating decision-making. Data Warehousing: Design of Data Warehouses (DataVault methodology) and development of Data Marts for various business domains (marketing, cinemas, finance). Azure and Big Data: Participation in implementing solutions on Azure Databricks, integrating innovative tools to optimize data analysis. Impact: Improved data quality, reduced errors, and overall time savings in business activity monitoring processes.
Equans
Data Engineer
ENERGY AND UTILITIES
April 2022 - June 2023 (1 year and 2 months)
Architecture and Storage: Design and structuring of sustainable database architectures, facilitating data management and utilization. Visualization and Reporting: Creation of interactive dashboards (Power BI) offering a consolidated view and accelerating decision-making. Applications and Collaboration: Development of a personnel management application (Power Apps) to optimize team productivity. R&D and Advanced Projects: Execution of POCs (proof of concept) around Python algorithms (e.g., vibration prediction), and presentation adapted to various audiences. CI/CD and Code Quality: Use of GitHub to correct, version, and ensure the quality of Python code, writing specifications to clarify objectives. Consulting and Change Management: Supporting stakeholders in adopting data solutions (architecture, best practices) and raising awareness of DataOps culture.