Senior Data Specialist
Posted Jul 27, 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 complex data problem you solved that others had struggled with.
What they are checking: Depth of problem-solving and the ability to explain a hard technical issue clearly.
Tell me about a time your data design had to change after it was built. How did you handle it?
What they are checking: Flexibility and care in managing changes that affect downstream users.
What would you do
Two source systems disagree on a customer's address. How do you write the rule for which one wins?
What they are checking: Practical thinking about survivorship rules and documenting them so the logic isn't hidden.
A pipeline you designed is producing duplicates in production. How do you find and fix the cause?
What they are checking: Structured troubleshooting and understanding of keys, joins and idempotent loads.
How do you balance data governance rules with teams that want data fast?
What they are checking: Ability to make governance practical so it protects quality without becoming a bottleneck.
About the work
Walk me through how you'd write a data pipeline specification with field-level mapping, business rules and data quality rules.
What they are checking: Whether their specs are precise enough that an engineer can build the pipeline without guessing.
How do you build a logical data model for a new domain, and how do you pick candidate source systems?
What they are checking: Real data modeling skill and the judgment to choose authoritative sources over convenient ones.
What makes a good canonical format, and how do you get teams to adopt it?
What they are checking: Understanding that canonical models only help if they're practical and agreed on across teams.
How would you design an ETL solution that scales as data volumes from connected machines grow?
What they are checking: Awareness of incremental loads, partitioning and failure recovery in large-scale pipelines.
How do you handle metadata management so people can find and trust data?
What they are checking: Whether they see metadata and governance as what makes data usable, not paperwork.
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Practice this out loudThe full job description
As published by Caterpillar.