Freelancer profile translated to English.
Back to original languageAbout Amine
Senior & Lead Data Engineer Expert AWS and Databricks (6 years of experience)
Expert in data engineering, I support companies in industrializing their AWS platforms and deploying high-performance Spark pipelines. My approach is based on a deep mastery of the Databricks ecosystem and complete automation via Terraform (IaC) and robust CI/CD chains.
Data Platform: Databricks Architecture (Lakehouse), Delta Lake, Unity Catalog, Spark SQL, PySpark.
AWS Cloud Ecosystem: Mastery of critical AWS services: AWS S3, AWS Glue (ETL), AWS Lambda, AWS Redshift, AWS Kinesis (Streaming), AWS IAM, AWS Athena, AWS Step Functions.
Data Processing: Spark cluster optimization, Spark memory management, distributed processing with PySpark.
Orchestration: Implementation of complex workflows with Airflow and AWS Step Functions.
Infrastructure as Code and CI/CD (DataOps): Total automation of AWS environments via Terraform and CloudFormation scripts.
CI/CD Automation: Setup of CI/CD pipelines (GitLab CI, GitHub Actions, Jenkins) for continuous deployment of Spark code and AWS infrastructure.
Containerization: Use of Docker and Kubernetes for isolating Spark processing.
Lead and Senior Achievements:
Lead Data Engineer (Caisse des Dépôts): Technical leadership for industrialization on AWS. Deployment of 20+ deliverables via CI/CD. Implementation of Spark and Python standards to ensure the reliability of AWS pipelines in a banking environment.
Senior Data Engineer (GRDF): Development of Spark pipelines on AWS (Kinesis, Glue, S3)
Data Engineer (Decathlon): Optimization of Spark jobs on the Databricks platform
Data Quality: Automation of data checks within Spark pipelines.
Leadership: Technical scoping for AWS architectures, selection of appropriate AWS services, and mentoring on CI/CD and Terraform (IaC) best practices.
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 20km)
Experience
- Societe generaleData Platform EngineerDIGITAL AND ITMay 2026 - Today (3 months)Paris, FranceSenior / Lead Data Engineer focused on building reliable and production-grade data platforms at scale.I design and implement distributed data processing pipelines with a strong emphasis on data quality, governance and long-term maintainability.My goal is to transform raw data into trusted, traceable and business-ready datasets used for analytics, BI and regulatory purposes.I also contribute to the design and industrialization of cloud-native data platforms, with a strong focus on infrastructure as code, automation and secure multi-cloud environments (Azure / AWS, Databricks).I have worked on critical platforms requiring:scalable processing, monitoring and observability, data reliability and traceability, and automated deployment and continuous deliveryas well as platform engineering challenges such as infrastructure provisioning, secure networking, and CI/CD for data platforms.Areas of expertise :Data Engineering: ingestion, transformation, historization and data exposureCloud & Data Platforms: Azure & AWS, Lakehouse architectures (Delta Lake), Databricks platformsInfrastructure as Code: Terraform, automated provisioning of cloud resources and Databricks environmentsPython & PySpark: distributed pipelines and performance optimizationCI/CD & Engineering practices: testing, quality gates, deployment automation and multi-environment workflowsData Governance: data quality, lineage, privacy, cataloging and Unity Catalog implementationAnalytical modeling: Lakehouse and Data Vault approachesData Platform Ops: monitoring, RUN, cost optimization (FinOps), and platform reliabilityMain stack :Python • PySpark • SQL • AWS • Azure • Databricks • Spark • Terraform • CI/CD • Airflow • Delta Lake • Unity Catalog.
- Caisse des dépôtsLead Data EngineerBANKING AND INSURANCEJanuary 2024 - January 2026 (2 years)Paris, FranceLeadership and industrialization of data solutions for the payment, investment banking, and regulatory reporting sectors (European Commission)• Functional scoping and technical data referent role (Workshops, POCs, Coordination of business and technical teams)• Design and implementation of Lakehouse architectures for analytical and regulatory uses• Development and industrialization of end-to-end Big Data pipelines (Spark, Python, SQL), with workflow automation• Implementation of CI/CD processes, Quality Gates, and Python craftsmanship best practices• RUN management (MCO), incident resolution, reliability, and continuous improvement of processing chains• Implementation of data privacy: anonymization, sensitive data cataloging, and associated governanceTechnical environment:Spark, Python, SQL, Data Lake, Data Vault, Data Marts, Hive, HDFS, Ranger, Atlas, Jenkins, CloudBees, Bitbucket, SonarQube, Control-M, SQL Server, Power BI, Tableau, Dataiku, Alteryx
- GRDFData EngineerENERGY AND UTILITIESSeptember 2022 - December 2023 (1 year and 3 months)Paris, FranceGazpar Project – Gazpar Project – Valorization and security of the data processing chain for IoT data from 11 million smart meters.● Design and development of massive ingestion pipelines for the historization of IoT frames (Wize 169 MHz protocol) into an AWS Data Lake● Implementation of an isolated and scalable Compute architecture using AWS EKS (Fargate) and Lambda for real-time parsing and aggregation of consumption indexes● Construction of reliable datasets on S3 for BI and Data Science needs● Pipeline orchestration with Airflow and complete infrastructure automation via Terraform (IaC) in CI/CD workflows.● Performance optimization and management of large volumes● Occasional use of CloudWatch and the ELK stack for monitoring and observability of the IoT chainTechnical environment:Python, SQL, PySpark, Spark, IOT Wize (169 MHz), AWS (S3, EKS Fargate, Lambda, KMS, VPC PrivateLink, IAM), Terraform, Airflow, Jenkins, GitLab, ELK, CloudWatch, Redis, Dataiku
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Education
- Master in Big Data and Data ScienceUniversité de Caen Normandie2020
- Bachelor's degree in Mathematics & Computer ScienceUniversité de Montpellier 22018