Model data for an ads platform

Quick Overview

This question evaluates proficiency in data modeling and database schema design for advertising systems, including entity-relationship modeling, privacy-aware user identifier handling, and delivery/tracking log representation.

Model data for an ads platform

Company: Netflix

Role: Software Engineer

Category: System Design

Difficulty: medium

Interview Round: Onsite

Design a **data model** (logical schema) for an advertising platform. Include core entities such as: - Business/account (advertiser) - Campaign and budget - Ad group / line item (if applicable) - Creative and assets - Targeting (audiences, geo, device, etc.) - User (or user identifiers) and privacy constraints - Delivery logs: impressions, clicks, conversions Explain: - Key relationships (1:N, N:M) and how you represent them. - How the schema supports the major flows: 1) **Intake/creation** flow (advertiser creates campaigns/creatives) 2) **Serving** flow (ad selection at request time) 3) **Tracking** flow (impression/click/conversion logging and attribution) - Any indexing/partitioning choices you would make for scale and reporting.

Quick Answer: This question evaluates proficiency in data modeling and database schema design for advertising systems, including entity-relationship modeling, privacy-aware user identifier handling, and delivery/tracking log representation.

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Jan 30, 2026, 12:00 AM
mediumSoftware EngineerOnsiteSystem Design
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Design a data model (logical schema) for an advertising platform.

Include core entities such as:

  • Business/account (advertiser)
  • Campaign and budget
  • Ad group / line item (if applicable)
  • Creative and assets
  • Targeting (audiences, geo, device, etc.)
  • User (or user identifiers) and privacy constraints
  • Delivery logs: impressions, clicks, conversions

Explain:

  • Key relationships (1:N, N:M) and how you represent them.
  • How the schema supports the major flows:
    1. Intake/creation flow (advertiser creates campaigns/creatives)
    2. Serving flow (ad selection at request time)
    3. Tracking flow (impression/click/conversion logging and attribution)
  • Any indexing/partitioning choices you would make for scale and reporting.

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