Akamai Data Engineer interview typically runs 6 rounds: HR phone screening, team lead call, two technical interviews, director interview, and final HR conversation. The process usually takes several rounds and is structured, professional, and pleasant.
$103K
Avg. Base Comp
$184K
Avg. Total Comp
6
Typical Rounds
3-5 weeks
Process Length
Our candidates report that Akamai’s bar for data engineering is grounded in real-world pipeline judgment, not puzzle-solving. The strongest signal is whether you can explain how data moves end to end: one candidate was asked to design an ETL flow in Excalidraw, and the conversation centered on components, dependencies, and how the pieces fit together. That tells us Akamai is listening for engineers who can structure a system clearly and make sensible tradeoffs, especially in a cybersecurity and data infrastructure context where reliability matters more than cleverness.
A recurring theme is the emphasis on practical Spark fluency. Multiple candidates described questions around PySpark DataFrames, simple transformations, and API design concepts, with little appetite for deep algorithmic tricks. We’ve also seen that interviewers tend to be easy to talk to and the process feels organized, which can lull candidates into underpreparing for the technical depth underneath the friendly tone. The people who do best here are the ones who can speak confidently about data movement, explain why a pipeline is built a certain way, and keep their answers concrete when asked to sketch or debug an ETL design.
Synthetized from 1 candidates reports by our editorial team.
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Real interview reports from people who went through the Akamai process.
The process was pretty structured and started with an HR phone screening, followed by an introductory call with the team lead. After that I had two technical interviews, then an interview with the director, and finally another HR conversation before the offer stage. The overall vibe was professional and pleasant, and the interviewers were easy to talk to, which helped a lot because the process was longer than I expected.
The technical part was less about grinding coding problems and more about practical data engineering fundamentals. One round covered API design concepts along with PySpark basics, especially DataFrames and simple transformations. The other stood out because I was asked to design an ETL process using Excalidraw, so it was very much a system-design style conversation where I had to think through the flow end to end and explain the components clearly. I didn’t get the sense they wanted deep algorithmic tricks; they cared more about whether I could structure a pipeline and speak confidently about Spark and data movement. I ended up not getting an offer, but the process itself was clear and well organized. If you’re interviewing here, I’d focus on being able to sketch an ETL architecture cleanly and talk through PySpark operations at a basic, practical level.
Prep tip from this candidate
Be ready to design an ETL pipeline on a whiteboard or Excalidraw and explain each stage clearly. Also review PySpark DataFrames and basic transformations, since that came up directly alongside API design concepts.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at Akamai
Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
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Synthesized from candidate reports. Individual experiences may vary.
The process starts with an HR phone screen to cover background, role fit, and basic logistics. This stage is described as structured and professional.
Next is an introductory conversation with the team lead. This call appears to be a general fit and team alignment discussion before the technical rounds.
One technical round focuses on practical data engineering fundamentals, including API design concepts and PySpark basics such as DataFrames and simple transformations. The emphasis is on applied knowledge rather than algorithmic coding.
A second technical round centers on system design for data pipelines, including designing an ETL process using Excalidraw. Candidates are expected to sketch the architecture end to end and explain data flow and components clearly.
After the technical rounds, there is an interview with the director. The experience suggests this is a senior-level discussion to assess overall fit and readiness for the role.
The process ends with another HR conversation before the offer stage. This likely covers final logistics, compensation, and next steps.