Senior Data Architect
Posted Aug 6, 2026 · We last checked this listing on Sep 20, 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.
Behavioral
Describe a time you translated a vague business requirement into a concrete data model. How did you handle ambiguity and get stakeholders aligned?
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?
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.
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?
communication skill across technical and executive audiences, called out explicitly in the posting
Technical
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?
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?
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?
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.
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?
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?
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?
genuine depth on modern data platform patterns versus buzzword familiarity
Situational
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?
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?
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?
ability to design for competing stakeholder needs, reflecting the cross-functional nature of the role
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