Data Engineer
Posted Sep 28, 2026 · We last checked this listing on Oct 6, 2026
Likely interview questions for this role
Written from this job description, not a generic list. Each one notes what the interviewer is really checking.
About your past
Tell me about a time a pipeline you owned failed in production. How did you find out and what did you fix?
What they are checking: Ownership and learning from failure, since this role works without close supervision.
The posting says you'd take on the more complex assignments with little supervision. Tell me about a project you drove start to finish on your own.
What they are checking: Their ability to scope, plan and deliver independently, which the posting expects at this level.
How do you document a pipeline so someone else can support it at 2 a.m.?
What they are checking: Whether they think about the people who maintain their work, not just about shipping it.
What would you do
An application team asks you to change a field in a data object that three other teams also use. What do you do?
What they are checking: Whether they manage schema changes carefully and communicate with every consumer before breaking them.
About the work
Walk me through a Python data pipeline you built from source to finished data object. What made it reliable?
What they are checking: Real pipeline experience in Python, the core tool named, and how they think about reliability.
What does a good business data object look like when applications depend on it?
What they are checking: Whether they design data products with clear definitions, stable schemas and consumers in mind.
With data from over 1.5 million connected assets, how would you handle late, duplicate or missing telematics records?
What they are checking: Their approach to messy machine data at scale, where bad handling silently breaks downstream applications.
How do you test a data pipeline so you catch problems before an application team does?
What they are checking: Data quality and testing habits, such as validation checks, unit tests and monitoring.
How do you decide between batch and streaming for a new data need?
What they are checking: Judgment about latency, cost and complexity rather than defaulting to one approach.
How do you write SQL or transformations that stay fast as the data keeps growing?
What they are checking: Performance awareness with partitioning, indexing and incremental loads on growing datasets.
Practice this interview out loud.
Offer builds a real interview for this exact role at Caterpillar from your resume and this job description, asks the questions one at a time, and tells you what landed. The first one is free.
Practice this out loudThe full job description
As published by Caterpillar.