Senior Data Architect
Posted Aug 7, 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
Describe a time you translated a vague business requirement into a concrete data model. How did you handle ambiguity and get stakeholders aligned?
What they are checking: ability to bridge business needs and technical modeling, a core responsibility here
Tell me about the most significant architecture decision you've made in a production cloud environment, ideally Azure. What was the outcome?
What they are checking: ownership and track record in cloud-scale architecture decisions, not just individual contributor tasks
Describe a situation where you mentored an engineer through a design review or code review that changed how they approached a problem.
What they are checking: genuine coaching ability versus a title-only claim of mentorship
Tell me about a time you had to explain a complex architecture decision to a non-technical audience, like leadership. How did you simplify it without losing the important parts?
What they are checking: communication skill across technical and executive audiences, called out explicitly in the posting
What would you do
Suppose your production datastore for telematics ingestion starts falling behind and data is arriving faster than it can be written. How do you diagnose and fix it?
What they are checking: practical performance tuning and troubleshooting skills under production pressure
If you inherited a legacy data platform that wasn't built for the scale it now handles, how would you approach re-architecting it without disrupting production?
What they are checking: how they balance modernization against operational risk in a live system
Imagine product and ML teams both want conflicting things from the same dataset, one wants low-latency access, the other wants deep historical analysis. How do you resolve that architecturally?
What they are checking: ability to design for competing stakeholder needs, reflecting the cross-functional nature of the role
About the work
Walk me through how you'd design a data platform to ingest high-volume telematics data from thousands of generators or battery systems in the field. What does the architecture look like from ingestion to serving?
What they are checking: whether they can reason through a real end-to-end IoT time-series architecture, not just name tools
Tell me about a time you had to choose between a relational database and a NoSQL store for a high-volume workload. What drove the decision?
What they are checking: depth of hands-on experience with real tradeoffs versus theoretical knowledge
How would you approach partitioning and indexing strategy for a time-series dataset that's growing by billions of rows a month?
What they are checking: concrete grasp of performance tuning at scale rather than textbook definitions
How have you used Infrastructure as Code, like Terraform, in managing data infrastructure? Give a specific example.
What they are checking: real hands-on IaC experience versus passing familiarity
How do you think about supporting ML teams from a data architecture standpoint? What do data scientists usually need that a typical operational data store doesn't provide?
What they are checking: understanding of ML-adjacent data needs like feature stores, training data access, and freshness
What's your approach to data governance and compliance, like GDPR, when designing systems that handle customer and equipment data?
What they are checking: awareness of privacy and regulatory obligations in data design, not just performance concerns
What does a lakehouse pattern actually solve that a traditional data warehouse or data lake alone doesn't, in your experience?
What they are checking: genuine depth on modern data platform patterns versus buzzword familiarity
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Practice this out loudThe full job description
As published by Generac.