Developer relations engineer domain knowledge interview questions

As asked

What is the OpenAPI Specification and what developer tooling does it unlock? Where does it fall short?

Sample answer outline

A strong answer explains OpenAPI as a machine-readable description of a REST API's endpoints, request and response schemas, authentication methods, and error codes. The tooling it enables: generated API reference docs, auto-generated SDK clients, Postman collections, mock servers, and linting. Shortcomings: it describes the HTTP surface but not semantic behavior or business logic, the spec can diverge from the actual API if not generated from code, and it does not handle GraphQL or event-driven APIs. On the version difference: OpenAPI 3.1 aligns fully with JSON Schema draft 2020-12, which means validation tooling that understands JSON Schema works directly with 3.1 specs, whereas 3.0 used a subset that differed in a few important ways such as nullable handling. OpenAPI 3.1 also adds native webhook support.

Expect these follow-ups

• How would you keep an OpenAPI spec in sync with the actual API implementation over time?

As asked

What is semantic versioning and what obligations does it place on an SDK or library maintainer? Where do you see teams get it wrong?

Sample answer outline

A strong answer explains major.minor.patch: patch for bug fixes that do not change the interface, minor for backward-compatible additions, major for breaking changes. The obligations: a 1.x user should be able to upgrade to any 1.y without code changes. Common mistakes: bumping minor for what is actually a breaking change because the author does not want to ship a major, or using pre-1.0 versions as a shield to break things freely for too long. They also mention that semantic versioning is a social contract, not enforced by tooling, so teams have to be disciplined about what constitutes a breaking change.

Expect these follow-ups

• How do you define a breaking change in the context of an SDK where some types are public but not officially documented?

As asked

What are the most important principles you apply when designing the public interface of an SDK, and which principle do you see violated most often?

Sample answer outline

A strong answer names concrete principles: the pit of success means the obvious way to use the SDK should be the correct and safe way; minimal surface area means you expose what is needed and hide implementation details; consistent naming follows the conventions of the target language rather than mirroring the HTTP API's snake_case if the language is Java; errors are never silent; and the SDK should work without reading the docs for the 80 percent use case. The most commonly violated: SDKs that mirror the raw API structure and force developers to know HTTP internals they should not have to care about.

Expect these follow-ups

• How do you handle the tension between consistency with the HTTP API and idiomatic design in the target language?

As asked

Can you explain the Diataxis documentation framework and give me an example of how you would apply it to a real set of developer docs?

Sample answer outline

A strong answer names the four quadrants: tutorials for learning, how-to guides for accomplishing specific tasks, reference for information lookup, and explanation for conceptual understanding. They explain the key insight that each quadrant serves a different user in a different mode, and that conflating them causes docs to fail at all of them. A concrete example would be applying it to an API: the authentication tutorial walks a new user through their first authenticated request, the how-to guide covers refreshing an expired token, the reference lists every auth endpoint with its parameters, and the explanation covers why the OAuth 2.0 flow was chosen and what it means for security.

Expect these follow-ups

• Is there a type of documentation that Diataxis does not accommodate well?

As asked

A developer asks you whether they should use webhooks or polling to receive updates from your API. How do you explain the tradeoffs in a developer guide?

Sample answer outline

A strong answer explains polling as simpler to implement but wasteful at scale, appropriate when the developer controls the schedule and latency requirements are relaxed. Webhooks push events as they happen, which is more efficient and lower latency but requires the developer to run an HTTP endpoint, handle retries and idempotency, and protect against spoofed payloads using signature verification. A good guide includes a decision tree, example code for each approach, and a section on webhook signature validation since that is the most common mistake developers make.

Expect these follow-ups

• How would you add server-sent events or WebSocket streaming to that comparison?

As asked

Your API supports OAuth 2.0 authorization code flow, client credentials flow, and device authorization flow. How do you explain to a developer which one they should use for their use case?

Sample answer outline

A strong answer routes the decision by use case: authorization code with PKCE for user-facing apps where the token represents an end user, client credentials for server-to-server integrations where there is no interactive user login, and device authorization for CLI tools or TV apps where the user cannot type a URL easily. They explain that choosing the wrong flow leads to security problems such as long-lived tokens stored in frontend code or unnecessary user prompts in a backend service. A good docs guide starts with the decision tree before showing code.

Expect these follow-ups

• What is PKCE and why is it important for authorization code flow in public clients?

As asked

How do you document rate limiting for an API in a way that is genuinely useful to a developer, not just a table of limits that they have to figure out how to handle themselves?

Sample answer outline

A strong answer proposes documenting: what the limits are and which dimensions they apply on such as per IP, per API key, or per endpoint, what the response headers tell the developer about their remaining quota, what a 429 response looks like and what they should do when they receive one, example code for implementing exponential backoff with jitter, and common patterns for staying under limits such as batching requests. They note that documenting the limits alone without the handling strategy causes developers to spam support when they hit a 429.

Expect these follow-ups

• How do you document rate limits that vary by pricing tier without making the docs feel like a sales brochure?

As asked

What does docs-as-code mean and why does a DevRel team benefit from treating documentation like source code rather than a wiki or a CMS?

Sample answer outline

A strong answer explains docs-as-code as storing documentation in a version-controlled repository alongside or near the code it documents, using pull requests for reviews, CI for validation, and automated deployment. The benefits for DevRel: engineers can submit docs PRs when they ship features rather than filing tickets for the docs team, changes are reviewed before publishing, the docs can be rolled back if an error ships, and version branches align docs with SDK releases. The main cost is that non-technical contributors find Git harder than a CMS, which needs to be accounted for in contributor guidance.

Expect these follow-ups

• How do you make a docs-as-code workflow accessible to a product manager or designer who does not use Git?

As asked

You are choosing a primary community platform for a developer product. Walk me through the tradeoffs between Discord, Slack, Discourse, and GitHub Discussions and when each is the right choice.

Sample answer outline

A strong answer compares on several axes: Discord and Slack are real-time chat which creates high engagement but low searchability and poor archiving; Discourse is forum-style with excellent search and SEO but requires more effort to get a question answered; GitHub Discussions lives where developers already are for open-source projects and integrates with issues, but is less suitable for general community conversation. The right choice depends on the nature of the questions: real-time support favors Discord, long-lived knowledge base favors Discourse, open-source project community favors GitHub Discussions. Many mature communities run more than one.

Expect these follow-ups

• How do you avoid fragmentation when your community is spread across two or three platforms?

As asked

Your API uses cursor-based pagination. How do you document this so a developer who only knows offset pagination can integrate it correctly without opening a support ticket?

Sample answer outline

A strong answer proposes explaining the why first: cursor pagination is more reliable on large datasets because offset results shift when items are inserted or deleted. Then it explains the mechanics: the first request returns data and a cursor token, subsequent requests pass that cursor in a query parameter until the response indicates no more pages. A good guide includes a complete working code example that iterates all pages, explains what happens at the last page, covers error cases such as an expired cursor, and compares the approach to offset so developers who know one can quickly grasp the other.

Expect these follow-ups

• How do you document pagination for a developer who needs to resume iteration after their process was interrupted?

As asked

How do you define the boundary between developer relations and developer support, and how do you operate the handoff between them without developers falling through the cracks?

Sample answer outline

A strong answer distinguishes DevRel as owning the proactive experience: docs, education, community, and feedback loops, while developer support owns reactive resolution of individual issues. The overlap is community forums, where DevRel often handles pattern-level questions and support handles case-specific escalations. The handoff works well when there is a clear escalation path, shared visibility into ticket trends, and a feedback loop where support surfaces patterns to DevRel for doc fixes. DevRel should monitor support queues to spot systemic issues, not to close individual tickets.

Expect these follow-ups

• How does this boundary change at a startup that cannot afford both functions?

As asked

What does a well-designed API error response look like, and what are the most common mistakes you see when reviewing an API's error design?

Sample answer outline

A strong answer names the components of a good error response: a stable machine-readable error code that clients can switch on, a human-readable message that explains what went wrong, a field-level detail for validation errors naming which field failed and why, a request ID for correlation with logs, and optionally a link to relevant documentation. Common mistakes: using HTTP status codes as the only error signal forcing clients to parse the body for specifics, inconsistent error schema across endpoints, returning HTML error pages instead of JSON from edge cases, and 200 responses that embed errors in the body.

Expect these follow-ups

• How do you advocate for better error design when the API is owned by a team that sees error messages as an afterthought?