Data Catalog Template for Engineering Teams

Begin with 10-20 datasets that your product and analytics teams query most frequently. Trying to catalog everything at once leads to shallow, incomplete entries that nobody trusts. A small, thorough catalog is more useful than a large, sparse one. Expand coverage each quarter.

The data platform or data engineering team typically maintains the catalog infrastructure, but individual dataset entries should be owned by the team that produces and maintains the data. Product analytics owns behavioral data, finance owns revenue data, and engineering owns system telemetry. The [data governance template](/templates/data-governance-template) covers the organizational structure for data ownership.

Three practices help. First, integrate catalog updates into your definition of done for any pipeline change. Second, automate what you can by syncing descriptions from dbt models or warehouse metadata. Third, run a quarterly review where each dataset owner verifies their entries. Stale catalogs lose trust fast.

For teams with fewer than 50 key datasets, a structured spreadsheet or Notion database works fine. This template gives you that structure. For teams with hundreds of datasets across multiple warehouses, tools like DataHub, Atlan, or Alation add automated lineage, search, and integration features. Start simple, migrate when the maintenance burden of a manual catalog exceeds 2 hours per week.

A data dictionary documents the schema of a single database or table: column names, types, and constraints. A data catalog is broader. It inventories all data assets across systems, adds business context (who owns it, what questions it answers, how fresh it is), and connects datasets to their consumers and lineage. Think of the dictionary as one section within each catalog entry. ---