Technical practice
How would you define a current-state and future-state Data & AI architecture?
What to coverThe role owns an architecture strategy and roadmap aligned with business and technology strategy. Suggested preparation: explain how you would assess existing platforms and data flows, identify gaps, and sequence changes by business value and delivery constraints.
How would you choose a cloud lakehouse architecture for an enterprise data platform?
What to coverThe posting seeks experience delivering cloud data platforms and lakehouse architectures, naming Azure, AWS, GCP, Databricks and Snowflake as examples. Suggested preparation: compare storage, processing and integration choices against workload needs, security, scalability and cost rather than assuming one named platform is required.
How would you design data models and integrations across multiple data domains?
What to coverThe role provides architectural leadership across domains, models, integrations and data flows, with an emphasis on governance. Suggested preparation: address shared definitions, ownership and data quality alongside the trade-off between reusable models and domain-specific needs.
How would you diagnose an unreliable data-ingestion pipeline?
What to coverThe role guides pipeline design and is accountable for scalable, secure ingestion. Suggested preparation: walk through how you would locate failures, assess their effect on downstream data, restore reliable processing and prevent recurrence.
How would you balance platform scalability, security, cost and enterprise standards?
What to coverAll four are explicit considerations for the advertised platforms and ingestion. Suggested preparation: describe how you would compare design options, document trade-offs and revisit decisions as workloads change.
How would you rationalise overlapping data stores without disrupting useful workloads?
What to coverThe role calls for modernising stores and platforms to reduce duplication and promote reuse. Suggested preparation: discuss how you would identify overlap, choose a target architecture and plan migration while accounting for dependencies.
What makes a data foundation ready for advanced analytics and AI?
What to coverBuilding AI-ready data foundations is a stated responsibility. Suggested preparation: connect data quality, discoverability, governance and dependable access to the needs of analytics and AI solutions, distinguishing foundational work from a specific model choice.
How would you design and evaluate an LLM solution using RAG and a vector database?
What to coverThe posting explicitly names LLMs, RAG and vector database architectures. Suggested preparation: explain retrieval and indexing choices, how you would assess answer quality, and the trade-offs among relevance, access controls, latency and cost.
How would you take an AI or machine-learning solution from design into operation?
What to coverThe role asks for experience designing and deploying AI, machine-learning and generative-AI solutions, plus knowledge of MLOps. Suggested preparation: cover evaluation before deployment, monitoring after release and how you would respond when performance or data changes.
When would you implement a solution yourself rather than delegate its design or delivery?
What to coverThe architect is expected to remain hands-on, with capability in Python, Java, JavaScript or C#. Suggested preparation: describe a bounded technical contribution you could make, how you would validate it, and how you would keep the wider architecture coherent.
Behavioural practice
Tell me about a time you developed a data or AI team while delivering complex work.
What to coverLeading, mentoring and developing professionals is a stated responsibility. Suggested preparation: use a real situation, your specific coaching and delegation actions, and the resulting change in team capability or delivery.
Describe a time you influenced senior stakeholders on a consequential architecture choice.
What to coverThe role works with senior business and technology leaders and connects technical choices to business strategy. Suggested preparation: give the situation, how you explained options and handled differing priorities, and the decision or outcome.
Tell me about a time you balanced a long-term architecture roadmap with an immediate delivery need.
What to coverThe posting explicitly calls for both strategy and practical, hands-on delivery. Suggested preparation: explain the competing demands, the action you took to protect future options while making progress, and the result.
Describe a time you gained adoption of a shared data architecture or standard.
What to coverThe role establishes best practice and leads architecture across domains, platforms and integrations. Suggested preparation: set out the situation, how you worked with affected teams to address resistance or constraints, and the outcome for consistency or reuse.