A pull-request description for data code is a structured, reader-friendly summary that explains the intent, context, and validation of changes to analytics pipelines, SQL or ETL code so reviewers can assess impact quickly and safely.
In modern data engineering, we ship changes through pull-requests (PRs): dedicated branches containing commits that modify SQL, Python, dbt models, orchestration DAGs, or infrastructure code. While the diff shows what changed, reviewers often struggle to understand why it changed, how it was tested, and what downstream impact it could cause. A well-crafted PR description closes this gap, reducing review time, preventing regressions, and documenting decisions for posterity.
Summarise the change in 50–70 characters, starting with a verb. Example: Refactor revenue attribution logic to support multi-channel
.
Answer “why?” in 2–4 sentences. Reference Jira tickets, incidents, or product requirements. Explain the business question or bug driving the change.
Bullet what you actually changed—new models, modified columns, removed dependencies. If touching multiple layers (SQL + orchestration), group by layer.
Describe downstream effects: dashboards affected, Airflow tasks rescheduled, or schema migrations required. Include data volume or performance considerations when relevant.
Show how you verified correctness: unit tests, data quality assertions, backfills on sampled data, or comparison queries versus production.
Explain how to revert if issues arise. For example, “Deploying behind a feature flag” or “Previous model retained as revenue_v1
for two weeks.”
Include a reviewer checklist so teammates know what to focus on—SQL logic, naming conventions, privacy concerns, etc.
Attach lineage diagrams, query plans, or Grafana screenshots to visualise the change.
### Context
_Why are we doing this?_
Fixes PAY-432: multi-channel attribution generates duplicate revenue when customers engage via email + paid ads.
### Changes
* Modified dbt model `stg_orders` to de-duplicate on `session_id`.
* Added new model `int_channel_weights`.
* Updated Airflow DAG `attribution_daily` schedule to hourly.
### Impact
* Affects Looker dashboards: Marketing ROI, LTV.
* Historical revenue (last 90 days) shifts –2% on average.
### Validation
* dbt tests: 12 pass, 0 fail.
* Backfill on 5% sample matched expected totals ±0.1%.
* Query runtime improved from 8.2s to 6.9s.
### Rollback
Revert commit and disable DAG `attribution_daily` in Airflow.
### Reviewer Notes
- Focus on CTE `dedup_sessions` logic.
- Ensure `order_source` enum covers all channels.
Diffs explain what changed, not why. Business context, risk, and validation rarely live in code.
PR time is the cheapest moment for documentation. Future you will thank present you.
Data changes propagate to dashboards and ML models. Impact analysis and backfill strategies are unique to data workflows.
Suppose your team modifies a Snowflake UDF and several dbt models. Below is a shortened PR following the template above:
### Context
Customer Success flagged inflated renewal ARR after FY-end close. Root cause: discounts applied twice.
### Changes
* Snowflake UDF `apply_discount()` fixed rounding bug.
* dbt model `fct_arr` updated to call new UDF.
### Impact
* ARR decreases ~1.7% across 2023.
* Tableau dashboard `ARR by Customer` reflects new numbers.
### Validation
* Recalculated 10 customers manually; values match.
* Added unit test for `apply_discount(10, 0.15)` == `8.5`.
While PR descriptions live in Git platforms, the code they reference often originates in a SQL editor. Galaxy’s version-controlled Collections integrate with Git, making it easy to generate PRs directly from validated SQL snippets. The “Endorse” feature signals which queries are production-ready—information you can reference in the Impact and Validation sections of your PR description.
A perfect pull-request description for data code answers three questions: why did we change the data logic, what exactly changed, and how did we validate it. By following the template and best practices outlined above, you’ll accelerate reviews, safeguard data quality, and create durable documentation for your analytics stack.
Data pipelines power critical dashboards, experiments and machine learning. A malformed SQL change can silently corrupt metrics and steer decisions wrong. By writing thorough pull-request descriptions you surface context, quantify impact and outline validation upfront, turning peer review into a strategic gate, not a rubber-stamp. This practice reduces on-call incidents, accelerates merges, and preserves tribal knowledge—key advantages for any analytics-driven organisation.
Long enough to convey context, impact, and validation—typically 5–10 concise sections that fit within one screen without excessive scrolling.
When possible, keep logically coupled changes (e.g., new model + DAG) in one PR so reviewers see the full picture. Unrelated refactors belong in separate PRs.
Include a subsection under Validation: list backfill ranges, resource estimates, and commands used. Link to job run logs so SREs can monitor.
Yes. Many teams auto-sync merged PR descriptions into Confluence or a docs/
folder to create a searchable history of data changes.