A2MAC1

Data Science Engineer

Casablanca На місці Опубліковано Кві 27, 2026
ЛокаціяCasablanca
Формат роботиНа місці
Опубліковано27 квітня 2026 р.
Остання перевірка06 травня 2026 р.

We are seeking an experienced Data Science Engineer to join our growing Data & Insights team in Casablanca. In this role, you will be responsible for developing and maintaining scalable data pipelines and architectures, while enabling high-performance analytics and machine learning workflows across the organization.

You will work closely with engineering, product, and AI/ML teams to design and deploy advanced data solutions using the Microsoft Fabric ecosystem. Your expertise will help unlock the value of our data, driving business insights and supporting strategic decision-making through robust, production-grade data engineering.

This is a key position within our global AI and Data Engineering community, reporting directly to the Engineering Manager.

GROUP ACTIVITY

A2MAC1 is the leading provider of automotive benchmarking and data analytics worldwide.

Our solutions help OEMs and suppliers understand their competitive landscape, accelerate development, and make better strategic decisions based on reliable, structured, and actionable insights.

Through our digital platforms, data services, consulting expertise, and AI-driven solutions, A2MAC1 supports the world’s top automotive players.

With 700+ employees across Europe, Asia, and North America, A2MAC1 continues its strong growth, driven by innovation and customer success.

KEY RESPONSIBILITIES

  • Design, build, and maintain robust ETL/ELT data pipelines using Microsoft Fabric tools
  • Develop scalable data warehouse architecture for analytics and reporting
  • Collaborate with cross-functional teams (Data Science, AI, Product, Engineering)
  • Ensure data quality, security, and compliance across the data lifecycle
  • Integrate structured and unstructured data from multiple sources
  • Leverage Python and open-source libraries to enhance data workflows
  • Monitor and improve performance of data processing pipelines
  • Apply best practices in data governance and version control

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