Merouane BARAOUI

Led Big Data Platform Architecture and Implementation – Designed and implemented a Big Data platform for managing B2B electricity and gas data. – Audited existing systems including data models, ETL pipelines, code quality, orchestration, and CI/CD processes: – Data modeling and architecture review – ETL orchestration using Airflow – CI/CD pipelines and deployment processes Optimization of Databricks jobs vs. AWS Lambda for lightweight processing – Designed a new architecture and data model tailored to business needs: – Implemented Medallion architecture for structured, clean, and curated data layers – Built a cross-functional monitoring and data maintenance layer – Developed an event-driven orchestration layer using AWS Step Functions, Lambda, DynamoDB, and EventBridge – Managed progressive migration from legacy pipelines to the new architecture: – Continuous ingestion of electricity and gas consumption data – Reconfiguration and scaling of Databricks clusters based on workload and data volume – Optimization of ETL code and Databricks jobs for performance and cost efficiency Tech Lead Responsibilities – Led the international deployment of the Big Data platform (Belgium, USA): – Scoped data flows and integration requirements across energy systems – Prepared technical environments: – GitHub repository organization – CI/CD pipelines aligned with local standards (GitHub / Azure DevOps) – Databricks workspace setup (groups, instance profiles, service principals, init scripts) – Identified technical challenges and adapted architecture or added transitional layers when required – Integrated pilot data streams as part of the platform rollout – Recruited, trained, and mentored local technical teams, providing guidance and support for adoption – Technical Environment: Spark, PySpark, Python, SQL, Databricks, AWS (Athena, Glue, S3, DynamoDB, Lambda, Step Functions, SQS, EventBridge...), Git, GitHub, CI/CD, Terraform.

Data Migration from On-Premise to AWS Cloud – Conducted technical assessments including data modeling and architecture design. – Defined and designed the AWS-based data lake and planned its future evolutions. – Gathered and analyzed business requirements from cross-functional teams. – Modeled and loaded data into the data lake using best practices. – Developed ETL pipelines using AWS Glue, Apache Spark, and Python. – Automated workflows with AWS Step Functions. – Built scalable Python-based processes using AWS Lambda. – External Data Ingestion from Third-Party Providers – Designed and implemented external data ingestion systems: – Set up webhooks via API Gateway and Lambda functions. – Deployed SFTP servers using AWS Transfer Family. – Integrated multiple external data sources into the data lake securely and efficiently. CI/CD Integration & Infrastructure as Code – Implemented CI/CD pipelines using GitHub Actions and Terraform. – Wrote and maintained Terraform code to deploy and configure AWS resources. Designed build, release, and deployment workflows for cloud infrastructure. • Managed versioning and environment transitions (dev, test, prod). Technical Environment: Spark, PySpark, Python, AWS (Athena, Glue, S3, DynamoDB, Lambda, Step Functions, IAM Roles, API Gateway, Kinesis, Lake Formation, EventBridge, Secrets Manager...), Git, GitHub, CI/CD, Terraform, Docker.

Data Engineering – Analyzed business requirements and proposed suitable AWS-based data architectures. – Designed and implemented data models and cloud architectures tailored to project needs. – Processed and transformed raw data using AWS Glue, Lambda, and Python. – Published curated datasets via internal data catalogs for business use. Data Science Projects for Multiple Departments (HR, Sales, Contracts, etc.) – Conducted workshops with stakeholders to define needs and collect data sources. – Integrated and analyzed diverse datasets from various business units. – Cleaned, transformed, and prepared data for modeling and insights. – Developed and evaluated machine learning models tailored to business use cases. – Built data visualizations and dashboards; participated in model industrialization. Innovation & Internal Challenges – Participated in ENGIE Data Science Challenges and awareness sessions, including: – Solar irradiance prediction from satellite images using time series and image processing. – Customer churn prediction based on behavioral and transactional data. – AWS DeepRacer: Built and trained autonomous car models using reinforcement learning. Business Intelligence & Data Visualization – Designed and delivered reporting solutions for various ENGIE business units (HR, Finance, Operations). – Created interactive dashboards primarily using Power BI to address domain-specific challenges. Technical Environment: Python (Anaconda, Jupyter Notebook, Scikit-learn, TensorFlow, Keras), AWS (Glue, Lambda, S3), Power BI.

Drone Data Analysis & Deep Learning System Development – Processed and extracted features from drone-captured images and video streams. – Created and organized custom image datasets, tailored to predictive modeling needs. – Developed regression and classification models using Convolutional Neural Networks (CNNs). – Estimated positions and detected landing zones using computer vision and image processing techniques. – Contributed to building an autonomous perception pipeline for drone navigation. – Technical Environment: Python, TensorFlow, Keras, OpenCV, Linux, Docker, ROS (Robot Operating System)