How the interviewer opens it
“Design an agent that helps people analyze data in BigQuery.”
The prompt is vague on purpose. The first few minutes are yours: narrow it to the core features and agree on the numbers before you draw anything.
What to clarify first
- Problem framing and provenance
- Users and scale
- Read:write mix
- Catalog and business meaning
- Latency
- Query costs and limits
- Identity and authorization
- Model access and answer evidence
- Consistency, availability, and durability
- Out of scope
The answers stay with the interviewer. In a SysRound round, Alex answers them as an interviewer would.
Scope of a strong answer
Core requirements
- Interpret an analyst's question, find relevant authorized tables and definitions, and ask for clarification when needed
- Generate, validate, and execute bounded read-only BigQuery queries
- Return an explanation grounded in query results, with SQL, sources, and limitations
- Support durable asynchronous runs, clarification replies, status checks, and cancellation
Usually out of scope
- Building BigQuery or ingesting data into it
- Changing production tables, schemas, or permissions
- Arbitrary code execution, unrestricted browsing, or other autonomous actions
- Training a foundation model
- Scheduled reports and cross-warehouse queries
What interviewers probe
- Correct SQL is not necessarily the correct answer
- A query can compile, stay within budget, and return plausible numbers while using the wrong metric, date boundary, table grain, or join. The agent must distinguish missing business meaning from a fixable SQL error.
- Keeping model proposals inside the user's authority
- The model consumes user text, catalog descriptions, and data values, all of which can contain instructions. A helpful SQL-generation tool can become a data-exfiltration path if it runs with broader permissions or unrestricted functions.
- Enforcing spend limits across concurrent agent attempts
- A tiny returned result can require a huge scan. Repairs and concurrent workers multiply spend. Dry-run estimates do not enforce an aggregate tenant budget, and row-level access policies can hide the billed-byte statistics needed for exact settlement.
- Recovering long-running tool calls without duplicate execution
- The application, queue, model endpoint, and BigQuery do not share a transaction. A worker may lose the submission response even though the query is running, and query completion may precede a failure to generate the answer.
Common mistakes
- Treating the problem as a chatbot wrapper around unrestricted BigQuery SQL execution.
- Assuming executable SQL is semantically correct or that a plausible narrative proves causation.
- Using shared administrative credentials and trying to recover user authorization through prompting.
- Confusing output limits, dry-run estimates, and enforceable billed-byte limits.
- Ignoring concurrent reservations and uncertain jobs, or assuming billing statistics are always available and refunding budget when they are missing.
- Adding multiple autonomous agents, vector databases, or model training before establishing a safe bounded workflow and measurable need.
The bar by level
- Senior (L5/E5)
- Builds a bounded agent workflow with clarification and stop conditions, separates semantic checks from syntax checks, preserves user-level BigQuery permissions, enforces atomic budget reservations and server-side query caps, and recovers asynchronous jobs using persisted identities. Settles conservatively at the reserved cap when actual billed bytes are unavailable. Explains why partial results and successful execution can still yield wrong answers.
- Staff (L6+/E6+)
- Defines explicit authority, spending, and recovery invariants across model calls, application state, and BigQuery. Handles policy changes on saved evidence, duplicate delivery, cancellation races, and idempotent budget settlement across accounting days. Recognizes that row-level policies can suppress billing statistics, separates conservative accounting from actual billing, and quantifies the resulting admission trade-off without weakening authorization. Proposes practical semantic, adversarial, and failure-recovery evaluations while keeping the architecture proportional to the stated scale.