Capgemini Data Engineer Interview Guide

The frustrating part was not the technical content but the compensation discussion at the end. I went through four interviews over roughly a month, split between introductory conversations and technical discussions. The technical side was geared toward my current project and domain, with an emphasis on problem-solving and architecture rather than just reciting definitions. I was asked about the main components of a computing system, what BSS is, and even the volatile keyword in C, so the scope was broader than a narrow data-engineering stack.

For the more domain-specific technical discussions, the questions were scenario-based and focused on SAP Datasphere-style work. I would expect to explain the architecture and practical choices such as Replication Flow versus Data Flow, federation versus replication, BW Bridge, space management, Analytical Models, Graphical Views, SQL views, delta loads, connectivity through Cloud Connector or DP Agent, authorizations, and performance tuning. The interviewer also used troubleshooting prompts, such as how I would investigate a Graphical View that contains data while the associated Analytical Model is blank, how to optimize ACDOCA-based models, and how to deal with joins that introduce duplicates. Be ready to talk through your reasoning and tradeoffs, not just give a definition.

After the final interview, they offered compensation below the minimum I had clearly stated in the first conversation a month earlier. I declined because of that mismatch. My main takeaway is to confirm the salary range again before committing to the later rounds, and, if the role is SAP Datasphere-focused, practice explaining end-to-end troubleshooting for data-flow, modeling, replication, and performance scenarios.