At Atos, a global leader in digital transformation, high-performance computing, and cloud infrastructure, the role of a Data Engineer is foundational to delivering enterprise-scale digital solutions. Atos operates as a trusted partner for multinational corporations, public sector entities, and financial institutions, helping them migrate legacy systems to modern cloud environments and leverage their data for strategic decision-making. As a Data Engineer, you are responsible for designing, building, and optimizing the robust data pipelines and storage solutions that make these transformations possible.

The impact of this role is highly visible. You will work on projects that directly influence client success, ranging from real-time streaming analytics to massive multi-cloud data migrations. Atos is widely recognized as a highly educational environment ("montée en compétence"), offering engineers a structured path to upskill in cutting-edge cloud architectures, big data frameworks, and modern data warehouse technologies.

Whether you are optimizing Spark jobs for a major European utility company or structuring a secure data lake for a financial institution, your work ensures that data is clean, accessible, and secure. Joining Atos as a Data Engineer means stepping into a dynamic, project-driven consulting environment where your technical expertise directly drives digital modernization on a global scale.

The interview process at Atos evaluates both your core technical capabilities and your project delivery experience. The questions below are compiled from real interview experiences of candidates globally and represent the patterns you should prepare for.

PySpark & Big Data Processing

This category evaluates your hands-on experience with distributed computing, framework internals, and data manipulation.

• How do you handle corrupt records when loading data in PySpark? What are the differences between the available handling modes?
• Explain how data serialization works in Apache Spark and how it affects job performance.

Preparing for an interview at Atos requires a balanced approach. You must demonstrate deep technical knowledge while showing that you can adapt to different client environments and project requirements.

Role-Related Knowledge – You must show a deep, precise understanding of big data frameworks (particularly PySpark and SQL) and cloud ecosystems. Interviewers often look beyond conceptual answers to test your knowledge of specific syntax, configurations, and API parameters.

Project Delivery and Architecture – Because Atos is a global systems integrator, you must be able to articulate the "why" behind your engineering choices. Be prepared to discuss your past projects in structured detail, highlighting your specific contributions and the business value delivered.

Adaptability and Growth Mindset – Atos values candidates who view the company as a learning environment. Emphasize your ability to quickly master new tools, adapt to shifting client requirements, and drive your own technical upskilling.

The interview process for a Data Engineer at Atos is structured to evaluate both your immediate technical readiness and your long-term potential within client-facing teams. While the exact steps can vary slightly depending on the country and specific business unit, the overall flow remains highly consistent globally.

Typically, the process begins with an initial screening by HR or a recruiting manager to assess your background, motivation, and location-specific requirements (such as language proficiency for European offices). This is followed by a technical assessment phase, which often includes a detailed technical interview focused on your core programming and data processing skills. The process concludes with a managerial or client-fit round to evaluate your communication, project management capabilities, and alignment with the team’s ongoing projects.

This visual timeline illustrates the typical path a candidate takes from the initial application to the final offer stage. Use this sequence to pace your preparation, focusing heavily on technical fundamentals in the early stages and shifting toward project storytelling and behavioral alignment as you progress. Note that in some regions, the technical evaluation may be split into a filtering round followed by a deeper architectural discussion.

PySpark & Big Data Engineering

The technical core of the Atos data engineering interview centers heavily on your ability to process data at scale. You will be evaluated on your practical knowledge of distributed computing frameworks, with a strong emphasis on PySpark.

Be ready to go over:

• Corrupt Record Handling – Master the specific syntax and behavior of Spark's parser modes, such as PERMISSIVE (with _corrupt_record columns), DROPMALFORMED, and FAILFAST.
• Performance Optimization – Understand broadcast joins, caching strategies, and how to identify and resolve data skew in Spark clusters.
• Schema Enforcement – Know how to define explicit schemas using StructType and StructField rather than relying solely on schema inference.
• Advanced concepts (less common) – Custom User Defined Functions (UDFs) optimization, Spark memory management (execution vs. storage memory), and integration with Delta Lake.

Example scenarios:

• "You are reading a massive, dirty CSV file where 5% of the rows have malformed structures. Explain the exact PySpark code and configuration you would write to isolate these corrupt rows without crashing the entire job."
• "How would you optimize a pipeline that is running slowly due to severe data skew on a join key?"

Project Walkthrough & Technical Approach

Interviewers at Atos place a high premium on your ability to describe your past work. They will look for structured, logical explanations of the data architectures you have built.

Be ready to go over:

• End-to-End Data Flow – Clearly explain how data moved from source systems to target destinations in your previous projects.
• Technology Selection – Be prepared to justify why you chose specific cloud storage, compute, and orchestration tools.
• Challenge Resolution – Have concrete stories ready about technical bottlenecks, data quality issues, or migration challenges you successfully resolved.

Example scenarios:

• "Describe a major data migration project you contributed to. What was the source architecture, what was the target cloud environment, and how did you ensure zero data loss during the transition?"
• "Walk me through how you designed a monitoring and alerting system for a critical daily ETL pipeline."