Section 18 · Performance & Analytics
Analytics
Server-side tracking, data pipelines, reporting, and engineering metrics
This guide walks you through auditing a project's analytics capabilities, covering data ownership, pipelines, reporting, and engineering metrics.
The Goal: Data You Own and Query
Analytics locked in third-party dashboards limits what questions you can answer. True data ownership means server-side capture, warehouse storage, and self-serve querying across product and engineering metrics.
- Owned — analytics data captured server-side and stored in infrastructure you control
- Queryable — automated pipelines flow data to a warehouse where SQL answers any question
- Self-serve — BI reporting layer enables non-engineers to build their own reports
- Engineering-aware — PRs, commits, and cycle time tracked alongside product metrics
Before You Start
- Identify the analytics stack (PostHog, Segment, Mixpanel, custom, etc.)
- Identify the data warehouse (BigQuery, Snowflake, Redshift, etc.)
- Get access to BI dashboards if external (Metabase, Looker, etc.)
Data Pipeline
Raw analytics data flows to a queryable warehouse (BigQuery, Snowflake, etc.) via automated pipeline. Data is queryable via SQL.
“Can you write SQL against your raw analytics events?”
GitHub data (PRs, commits, contributors) flows to warehouse for engineering metrics and correlation with product analytics.
“What's your team's actual PR cycle time this quarter?”
BI tool connected to data warehouse. Non-engineers can view dashboards and ideally create their own reports (self-serve).
“Can your non-engineers answer their own data questions?”