Design APIs and data flows for creator analytics used by internal and external teams

Problem Statement Description

You are designing the technical product foundation for a creator analytics platform that serves both external users, such as creators, agencies, and small businesses, and internal teams, such as sales, support, trust and safety, partnerships, finance, and product operations. The system must expose reliable analytics through APIs and data flows so different stakeholders can understand creator performance, audience engagement, monetization, campaign outcomes, and account health.

The core challenge is to define how raw events and business data become trusted, permissioned, and usable analytics across multiple surfaces: dashboards, partner integrations, internal tools, exports, alerts, and automated decision systems. External customers need clear, timely, and understandable metrics to make business decisions, while internal teams need deeper operational views without violating privacy, contractual, or access boundaries.

Assume the product operates at large scale across many creators, content types, geographies, devices, and monetization models. Data may arrive from event streams, transaction systems, content systems, ad/campaign systems, user profiles, and third-party integrations. Your design should account for inconsistent data quality, metric definition conflicts, latency expectations, backfills, consent, privacy, and different levels of API maturity across consumers.

The experience should consider:

- API consumers, including external creators, small-business customers, agencies, internal analysts, internal operational teams, and partner systems

- Core analytics entities and metric definitions, such as creators, content, audiences, campaigns, revenue, engagement, retention, and attribution

- Data flow from event collection through ingestion, processing, storage, aggregation, serving, and API access

- Requirements for freshness, historical accuracy, backfills, idempotency, pagination, filtering, versioning, and rate limits

- Authentication, authorization, role-based access, tenant isolation, privacy controls, auditability, and data-sharing boundaries

- Reliability and observability expectations, including data quality checks, API SLAs, anomaly detection, lineage, monitoring, and incident response

- Trade-offs between real-time versus batch analytics, flexibility versus consistency, and self-serve access versus governed usage

- Rollout approach for internal and external consumers, including migration, backwards compatibility, documentation, and developer support

Your goal is to frame a technical product design that enables high-trust creator analytics for decision-making while balancing scale, correctness, privacy, usability, and long-term platform extensibility.