CV Fleet Management : Sélectionnez facilement les profils qui vous intéressent

Aperçu d'expériences d'Abdelouahab,
freelance FLEET MANAGEMENT résidant dans l'Isère (38)

• Data Engineer

  DataImact by NielsenIQ
  2019 - aujourd'hui

  Sherlock Project : Leading on the Sherlock project, a cloud based analytics tool aimed
  at improving data quality and reducing workload. Closely collaborating with support and
  operations teams to ensure seamless integration and effective implementation.
  • Gain insight into business requirements and context in order to devise and deploy
  cloud solutions aimed at improving internal data quality.
  • Build and deploy monitoring tools to uncover data patterns and insights.
  • Connecting together over 10 data sources and APIs to one output.
  • Customized Sherlock to meet specific project requirements, tailoring monitoring
  rules and alerts to align with business needs.
  • The implementation of the Sherlock approach significantly decreased the operational
  workload time for the team, reducing it from 2 weeks to a mere 3 work hours.
  • Actively participated in regular reviews and retrospectives to identify areas for improvement within the Sherlock project.

  Tech stack : Streamlit, Polars, DuckDB, Looker Studio, Cloud Run, Big Query, GCP.

• Software Engineer

  Siemens SPA
  2018 - aujourd'hui

  SinOut: Development of SinOut a management application for employees building access
  events
  SFM: Developed Siemens Fleet Management, a fleet management web application for SCM
  Service (Supply chain Management), for the use of Siemens Algiers employees

• Client Requests Analysis : Categorisation of the recurring topics on clients requests.
  aujourd'hui

  Analysis based on client tickets from JIRA api.
  • Leveraging NLP methodologies to categorize tickets based on the semantic content
  of their descriptions.

  • Tech stack : Looker Studio, BigQuery, Google Sheets,Jira API, GCP.

• aujourd'hui

  Amazon S&S : ML model for Amazon Sales and Shares estimation. Building large scale
  processing pipelines for amazon.us data (Pandas, Dask, GCP).
  • Data Cleaning and processing for Amazon.US best seller data.
  • Implementing distributed solution with python DASK clusters.
  • Developed robust data monitoring solutions to track and validate data quality, ensuring accuracy and completeness in real-time.
  • Implemented data validation checks, anomaly detection, and automated alerting
  mechanisms, reducing the risk of data inconsistencies.
  • The process was deployed on GCP with full CI/CD implementation

  • Tech stack : InfluxDB, Grafana, JupyterHub, Prefect, Compute Engine, GCP.