How would you model cross-platform ad campaign data and adaptation logic?

WHAT THIS TESTS: This question tests your ability to design a layered architecture that insulates business concepts from external API volatility. The interviewer wants to see if you can model a single source of truth for campaign intent while respecting that each advertising platform has distinct object hierarchies, field limits, creative formats, and targeting rules. They are looking for evidence that you understand normalization, transformation pipelines, and operational concerns like partial failure and observability.

A GOOD ANSWER COVERS: A strong response starts with a canonical data model that captures the marketing team's high-level concept. This model should include campaign goals, audience segments, creative variants, and budget constraints without referencing any provider schema. Next, introduce a platform adaptation layer composed of provider-specific adapters or strategy objects. Each adapter knows how to map canonical fields into the target platform's native structures, such as Google Ads campaigns and ad groups or Meta ad sets and ads, while enforcing constraints like character limits and image aspect ratios. Then describe an asynchronous deployment pipeline that stages transformations, validates payloads against live or cached platform metadata, submits via each provider API, and captures execution state. Finally, explain how you handle drift when platforms change their APIs, such as versioning the adapter rules and using feature flags to roll out new mappings gradually.

COMMON WRONG ANSWERS: A red flag is proposing a single database table with nullable columns for every possible platform field, which creates a sparse unmaintainable schema. Another mistake is suggesting synchronous sequential calls to each ad API during the user's save action, which ignores latency and failure isolation. Some candidates also forget constraint enforcement entirely, assuming that a headline written for Google will automatically fit TikTok's length limits or creative specifications.

LIKELY FOLLOW-UPS: The interviewer may ask how you would handle a platform API deprecation without rewriting the entire system, or how you would support A/B testing a single creative variant across only two of five connected platforms. They might also probe how you would reconcile spend and performance data back into the canonical model when each platform reports metrics on different schedules and with different attribution windows.

ONE CONCRETE EXAMPLE: Imagine a summer sale campaign with a hero headline, two body variants, and a set of images. The canonical record stores the headline as Summer Sale: 50 Percent Off and tags the images as square and vertical. The Meta adapter splits this into a campaign, an ad set with lookalike targeting, and two ads with vertical video. The Google adapter creates a search campaign with responsive search ads, truncating the headline if it exceeds 30 characters and mapping body copy into description lines. The TikTok adapter requires a full-screen video, so it flags the vertical image for creative team review and pauses that platform's deployment while launching the others.