Data Impact by NielsenIQ

Data Impact by NielsenIQ

  • Big Data, E-commerce
  • Paris, Alger, London, New York
  • View website

Tech team

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

Employee breakdown

  • IT/Tech

    44%

  • Operations

    19%

  • Product

    1%

  • Sales & Marketing

    7%

  • CSM & Client support

    19%

Technologies and tools

  • 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%

Organization and methodologies

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.

Projects and tech challenges

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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