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Bakarime DiomandeBD

Bakarime Diomande

Data Engineer & Scientist | DataOps & MLOps | AWS

€700/day
Paris 13e Arrondissement, FR
3-7 years

Average response time: 1 hour

Freelancer profile translated to English.
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About Bakarime

Coming from a university background, I was predisposed to a career in research. After a few years as a research engineer in telecommunications network optimization, I transitioned to Cloud and Data. I have varied skills in the data field, covering a wide range from Data Engineer | DataOps to Data Scientist | MlOps. I have a solid background in AWS Cloud Solution Architecture with a strong appetite for the DevOps approach. I primarily work on designing and building data platforms in the cloud. I also work on the design and deployment of ML solutions, leveraging the following technologies:
- AWS
- Python
- Spark
- SQL
- NoSQL
- Terraform
- Databricks
- Airflow
- MLFlow
  • French

    Native or bilingual

  • English

    Fluent

  • Italian

    Basic

Can work on-site
Paris 13e Arrondissement (up to 50km)

Experience

  • ENGIE - Entreprises & Collectivités
    DataOps & DevOps
    ENERGY AND UTILITIES
    October 2021 - Today (4 years and 9 months)
    Bagneux, France
    Intervention 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
    Python Spark Terraform AWS Gitlab CI Airflow Databricks
  • VALOWAY
    DataOps & DevOps
    DIGITAL AND IT
    May 2021 - August 2021 (4 months)
    Paris, France
    As 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.
    Python Spark ETL AWS Data Architecture Data Analytics Gitlab-ci Terraform Agile methodology
  • Veolia Water technologies
    DataOps and DevOps
    ENVIRONMENTAL
    October 2019 - April 2021 (1 year and 6 months)
    Saint-Maurice, France
    As 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.
    Python Spark ETL SQL NoSQL AWS Data Architecture Data Analytics Gitlab-ci Terraform

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Education

  • Bachelor
    Cadi Ayyad University of Marrakech (Morocco)
    2006
    Probabilité et Statistique
  • Master
    Cadi Ayyad University of Marrakech (Morocco)
    2008
    Mathématiques Appliquées et Modélisation

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