About Bakarime
French
Native or bilingual
English
Fluent
Italian
Basic
Experience
- ENGIE - Entreprises & CollectivitésDataOps & DevOpsENERGY AND UTILITIESOctober 2021 - Today (4 years and 9 months)Bagneux, FranceIntervention on various DGP projects:• Agile Methodology (backlog, scoring, sprint, retrospective)• Deployment of the AWS stack with Terraform (S3-LAMBDA-GLUE, etc...)- Ensuring consistency and reproducibility with Terraform modules- Automated provisioning and scaling with Terraform and GitLab• Maintenance in operational condition of the data infrastructure on AWS- Management of access controls, security group configurations, encryption configuration- Use of AWS Inspector to detect vulnerabilities in AWS AMIs- Patching of EC2 instances with non-vulnerable AMIs- Implementation of high availability by deploying in multiple regions, using auto-scaling and configuring automatic failover- Implementation of SSL compliance for S3 buckets to enhance security- Cost management by implementing cost-saving strategies- Monitoring resource performance and health with CloudWatch- Implementation of backup strategies for data and resource configuration• Configuration of Databricks infrastructure on AWS using Terraform- Create network infrastructure (VPC, subnets, VPC endpoint)- Create Databricks resources (workspace, storage configuration, Unity Catalog configuration)- Configuration of a CI/CD pipeline with GitLab to create Databricks clusters with Spot instances• Configuration of AWS-managed Airflow with Terraform- Create an MWAA environment- Create DAGs for MWAA• Orchestration of Databricks tasks using Airflow• Deployment and operation of the ELK stack for resource monitoring- Configuration of CloudWatch subscription to send logs and metrics to ELK• Configuration of Elasticsearch as Grafana data sources for dashboard creation
- VALOWAYDataOps & DevOpsDIGITAL AND ITMay 2021 - August 2021 (4 months)Paris, FranceAs part of the Forkast project:• Agile Methodology (backlog, scoring, sprint, sprint retrospective)• Design of Data Architecture and Infrastructure- Data modeling by identifying relevant data entities and understanding the relationships between them.- Storage architecture by defining, considering data volume, type of storage technologies (AWS S3 datalake), and how data will be stored and retrieved.- Design of data integration by choosing AWS Glue ETL solution due to its serverless nature and data volume.• Configuration of the pipeline for data ingestion and processing- Configuration of the AWS Glue catalog, database, and jobs.• Data ingestion and processing with Lambda (Python3) and Glue (Pyspark)- Use of AWS Lambda (Python) to check different file formats and extract relevant data from files, then create a JSON output file.- Use of Glue (Pyspark) to deduplicate and validate data type formats.- Use of Glue (Pyspark) to aggregate real-time data into daily, weekly, etc. data.• Deployment of the stack (S3-LAMBDA-GLUE-DYNAMODB) with Terraform• Configuration of the CI/CD pipeline with GitLab-CI- Configuration of AWS credentials in GitLab.- Creation of the CI/CD deployment pipeline with .gitlab-ci.yml.
- Veolia Water technologiesDataOps and DevOpsENVIRONMENTALOctober 2019 - April 2021 (1 year and 6 months)Saint-Maurice, FranceAs part of the Datalake and Datahub project:• Agile Methodology (backlog, scoring, sprint, sprint retrospective)• Design of Data Architecture and Infrastructure- Data modeling- S3 storage architecture- Design of data integration with AWS Glue ETL• Configuration of the pipeline for data ingestion and processing- Configuration of the AWS Glue catalog, database, and jobs.• Data ingestion and processing with Lambda (Python3) and Glue (Pyspark)- Use of AWS Lambda (Python) to check different file formats and extract relevant data from files, then create a JSON output file.- Use of AWS Lambda (Python) to insert data into DynamoDB- Use of Glue (Pyspark) to perform data quality checks (removal of null values and duplicates, validation of data types, checking if data contains relevant fields, etc.).- Use of Glue (Pyspark) to aggregate real-time data into daily, weekly, etc. data.• Querying MySQL and PostgreSQL databases with SQL.• Deployment of AWS resources with Terraform- Processed data available for display via API Gateway backed by AWS Lambda retrieving data from DynamoDB.- Processed data available for AI via a Glue job that creates gold data on S3.• Deployment of AI solution (SAGEMAKER MLOPS FRAMEWORK)- Configuration of instances with auto-scaling for model training.- Creation of artifacts for the trained model with model parameters and metadata.- Deployment of the trained model to the SageMaker endpoint.- Performance monitoring with CloudWatch.- Model version management with SageMaker.• Unit tests with Moto, Boto3, and Pytest.• Configuration of the CI/CD pipeline with GitLab-CI.
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Education
- BachelorCadi Ayyad University of Marrakech (Morocco)2006Probabilité et Statistique
- MasterCadi Ayyad University of Marrakech (Morocco)2008Mathématiques Appliquées et Modélisation
Certifications
- AWS Certified SysOps Admin AssociateAmazon Web Service2018
- AWS Certified Solution Architect AssociateAmazon Web Service2019