This hub maps the LinkedIn Ads pipeline from API access through dbt transformation. LinkedIn requires a manual API review, has OAuth tokens that expire on a human timescale, and an analytics endpoint with several structural constraints. Professional demographic pivots (company, seniority, job function, industry) are unique to LinkedIn among major ad platforms.
Getting Access
The manual review process is the longest lead-time item in the pipeline.
- LinkedIn Ads API Access — Developer app setup, the manual review process, what to include in your application, rejection handling, and the additional access layers (Lead Sync API, private APIs, Company Intelligence API).
Authentication and Token Management
The ongoing operational concern once you’re approved.
- LinkedIn Ads OAuth Token Management — 60-day access token expiry, 365-day refresh token expiry, why there’s no service account alternative, and how to build operational processes around the forced annual re-auth.
The Analytics Endpoint
Where extraction complexity lives.
- LinkedIn Ads Analytics Endpoint — No pagination, the 15,000-element cap, the 20-metric-per-request limit, query tunneling for long URLs, the January 2024 cursor pagination migration, monthly API versioning, and demographic data constraints.
What the Data Is Actually For
The B2B case for building this pipeline despite the friction.
- LinkedIn Ads B2B Data Value — Professional demographic pivots (MEMBER_COMPANY, MEMBER_SENIORITY, MEMBER_JOB_TITLE), the negative CTR-to-pipeline correlation, company-level impression attribution via the Company Intelligence API, and the 90-day attribution window.
Modeling in dbt
How to structure the transformation layer.
- LinkedIn Ads dbt Modeling — The campaign hierarchy rename (Campaign Group → Campaign, Campaign → Ad Group, Creative → Ad), metric normalization (clicks, spend, conversions definitions), dbt_ad_reporting package integration, the incremental strategy for 90-day lookback, and how to separate platform-specific models from the unified cross-platform layer.
Related Garden Notes
- Ad Platform API Landscape — How LinkedIn compares to Google, Meta, and other platforms at the API level.
- Ad Data Extraction Tools — Fivetran, Airbyte, dlt, and Supermetrics compared for LinkedIn-specific capabilities (lead form support, demographic handling, SCD2 history).
- Ad Pipeline Engineering Challenges — Rate limits, schema changes, attribution normalization, and privacy compliance across all ad platforms.
- Ad Platform Metric Divergence — Why LinkedIn impressions, clicks, and conversions aren’t directly comparable to Google or Meta, including the campaign hierarchy table.
- dbt Ad Reporting Patterns — The broader cross-platform dbt modeling context that LinkedIn fits into.
Source Article
linkedin-ads-pipeline-guide — “Why LinkedIn Ads data is the hardest to get right”