À propos de Soheil
With experience ranging from large international groups to fast-growing startups, I offer expertise on data engineering. As a freelancer, my goal is to collaborate with companies in developing effective data infrastructure solutions, and implementing the best known practices in terms of data pipeline development, data management, and governance. By leveraging the knowledge I've acquired over the course of my career, and my constant training in data engineering topics, I can help you to :
- migrate all your data models to dbt for better maintainability, testability, and governance.
- build and maintain new data pipelines and models with Airflow and dbt
- deploy and manage an Airflow environment on Amazon Web Services (AWS), Google Cloud Platform (GCP) or Astronomer
- build a data lakehouse architecture on AWS (with solutions like Glue, Redshift Spectrum, Lake Formation, Athena)
- implement DevOps best practices for deploying data pipelines and data models, such as settings up automated multi-stage deployments with multiple environments (dev, staging, prod) with GitHub Actions or Jenkins
- establish a data mesh structure in your organization and promote self-service analytics using the dbt Mesh pattern
- deploy a data integration platform with Airbyte or dlt, and build new integration pipelines
- build and deploy a React app with custom D3 visualizations.
Français
Bilingue ou natif
Anglais
Capacité professionnelle complète
Accepte de travailler sur site
Paris (jusqu’à 15 km)
Expériences
- LacosteData Platform Engineer / Finance Analytics EngineerMODE & COSMÉTIQUESaoût 2025 - Aujourd'hui (10 mois)Paris, Francedbt Cloud configuration and DevOps
- Set up dbt Cloud account from scratch, managing user permissions and setting up multiple environments (development, staging, and production).
- Built CI workflows for data quality, including code validation, unit tests, and data tests, and dbt_project_evaluator to monitor adherence to dbt best practices.
- Designed the deployment workflow across multiple environments.
Data modeling and optimization- Designed and optimized data models to enhance performance and reliability for Qlik dashboards.
- Shifted all metric computations and transformations from Qlik (QVDs) to dbt, centralizing business logic within the data warehouse.
- Built and maintained metrics definitions using dbt Semantic Layer solution to standardize KPIs across teams.
Training and knowledge sharing- Conducted training sessions on dbt best practices, data modeling layers (staging, intermediate, marts), and project structure.
- Supported the upskilling of analytics and data teams through workshops and ongoing mentorship.
Migration strategy and data mesh- Designed and executed a migration strategy leveraging dbt’s audit_helper to ensure a smooth transition.
- Implemented data mesh principles, enabling collaboration across domains via dbt Mesh, data contracts and model versioning.
Analytics engineering for finance team- Built and maintained a data pipeline to calculate the integrated margin using data coming from SAP, consolidating industrial, retail, and logistics margins into a unified financial metric.
- PrimelisData Engineer / Full-Stack DeveloperEDITION DE LOGICIELSjanvier 2025 - juin 2025 (5 mois)Levallois-Perret, FranceGeneric dbt package
- Developed a generic dbt package to compute marketing insights across multiple sources (Google Ads, Google Search Console, etc.) and deployed it for 80+ clients (including Smartbox, La Redoute, and UNICEF).
- Isolated client data by setting up separate environments per client on Google Cloud for enhanced security and scalability.
- Implemented automated anomaly detection using Elementary to monitor and alert on data quality issues.
- Built CI/CD workflows with GitLab CI to automate tests and enforce best practices via the dbt_project_evaluator package.
- Integrated dbt models with a frontend analytics dashboard, enabling real-time insights for end-users.
- Presented a PoC on SQLMesh to replace dbt (currently).
Orchestration and data observability- Deployed and configured Dagster Cloud environments to orchestrate and monitor dbt pipelines.
- Enabled data observability by configuring Elementary Cloud to track test results, run results, and dbt artifacts, improving monitoring and data quality insights.
- Optimized 80+ data pipelines (each with ~150 dbt nodes) to reduce build time to under 15 minutes, significantly improving iteration speed.
- AccorData Engineer / Full-Stack DeveloperHÔTELLERIEmai 2024 - mars 2025 (10 mois)Issy-les-Moulineaux, FranceData mesh with dbt and Astronomer
- Migrate all Snowflake stored procedures/tasks to dbt models.
- Set up and configured Airflow environments in Astronomer.
- Set up a data mesh with one dbt project for each feature team, and one dbt project for core models.
- Enabled seamless collaboration between feature teams using data contracts and groups in dbt.
- Implemented CI/CD workflows with GitLab CI to automate tests and deployments for all data projects.
- Presented a PoC on Dagster to replace Airflow (currently).
Data observability and semantic layer- Configured Elementary for data observability on test results, run results and dbt artifacts.
- Configured semantic models to test dbt semantic layer and Cube, and their integration with Tableau.
- Presented a PoC on DataHub to have a centralized data catalog (currently).
Training data/analytics engineers on dbt and Airflow- Conducted multiple workshops and presentations to keep data and analytics engineers updated on best practices and new features.
- Developers reported a significant increase in productivity.
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Formations
- Master's degree, Applied Mathematics (Specializing in Data Science)Université Grenoble Alpes2020Master's degree, Applied Mathematics (Specializing in Data Science)
- Master's degree, Computer Science (Specializing in Decision and Optimization)Université de Caen Normandie2019Master's degree, Computer Science (Specializing in Decision and Optimization)