Data Impact by NielsenIQ

Data Impact by NielsenIQ

  • Big Data, E-commerce
  • Paris, Alger, London, New York
  • Voir le site

L'équipe Tech

Data Impact engineering team is split on both sides of the Mediterranean Sea, in Paris and Algiers, with 30% of our developers being women. Our engineering team is a uniquely diverse and international environment, with an average age of twenty-eight and passionate about new technologies and innovation. Don't get it wrong, we know how to have fun as well !

Data Impact by NielsenIQ
Data Impact by NielsenIQ

Répartition des collaborateurs

  • Front-End

    44%

  • Back-End

    19%

  • Produit

    1%

  • Design

    7%

  • Support

    19%

Technologies et outils

Backend

  • Python
    Python
    90%
  • MongoDB
    MongoDB
    80%
  • Go
    Go
    60%
  • Symfony
    Symfony
    20%
  • PHP
    PHP
    20%
  • Node.js
    Node.js
    20%
  • MariaDB
    MariaDB
    20%
  • Elasticsearch
    Elasticsearch
    10%

Frontend

  • React JS
    React JS
    50%
  • JavaScript
    JavaScript
    50%

Devops

  • Ubuntu
    Ubuntu
    100%
  • Google Cloud Platform
    Google Cloud Platform
    60%
  • Kubernetes
    Kubernetes
    40%
  • Ansible
    Ansible
    10%

Data

  • CockroachDB
    CockroachDB
    100%

Organisation et méthodologies

Data Impact engineering team organises itself around the data lifecycle, from scraping to customer delivery. Each team works in a close partnership with their immediate counterpart, using the Scrum methodology. Each team is divided into smaller squads, some of them having a more transverse role. Every line of code goes through a code review, and every recent project has its ci/cd. Each squad organise their weekly public code review where they study one of the squad members particularly complex pull request. These process allow continuous delivery as well as making sure our codebase is shared between our developers.

Projets et défis techniques

Every day Data Impact collects more than 60 billion data points. The database includes 300+ different retailers in 40+ countries. We work with a complete data set – not a misleading sample. Our data is collected where consumers actually shop and it undergoes a thorough, human-validated cleaning process. We have developed proprietary AI and ML technology and are constantly innovating.

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