Writing the Perfect Pull-Request Description for Data Code

Overview

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.

Why Pull-Request Descriptions Matter

• Faster reviews. Clear context lets teammates focus on code quality instead of deciphering intent.
• Lower risk. Listing affected tables, metrics, or jobs surfaces hidden dependencies before they break dashboards.
• Better auditing. When data incidents happen, ops teams can trace when and why a transformation changed.
• Knowledge sharing. PRs double as lightweight architectural decision records (ADRs) that newcomers can search.

Core Elements of a Perfect PR Description

1. Title

Summarise the change in 50–70 characters, starting with a verb. Example: Refactor revenue attribution logic to support multi-channel.

2. Context & Motivation

Answer “why?” in 2–4 sentences. Reference Jira tickets, incidents, or product requirements. Explain the business question or bug driving the change.

3. Changes Introduced

Bullet what you actually changed—new models, modified columns, removed dependencies. If touching multiple layers (SQL + orchestration), group by layer.

4. Impact Analysis

Describe downstream effects: dashboards affected, Airflow tasks rescheduled, or schema migrations required. Include data volume or performance considerations when relevant.

5. Validation & Testing

Show how you verified correctness: unit tests, data quality assertions, backfills on sampled data, or comparison queries versus production.

6. Rollback Plan

Explain how to revert if issues arise. For example, “Deploying behind a feature flag” or “Previous model retained as revenue_v1 for two weeks.”

7. Checklist / Reviewer Guidance

Include a reviewer checklist so teammates know what to focus on—SQL logic, naming conventions, privacy concerns, etc.

8. Screenshots or Metrics (Optional)

Attach lineage diagrams, query plans, or Grafana screenshots to visualise the change.

Template You Can Copy

### 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.


Best Practices & Tips

• Write first, code second. Draft the description when opening the PR to clarify scope early.
• Keep it business-oriented. Data stakeholders care about metric movement; highlight it.
• Automate the boring parts. Use commit hooks or PR templates to pre-fill headers like “Checklist.”
• Link lineage. Tools like dbt docs, OpenLineage, or Galaxy Collections can auto-embed model dependencies.
• Update as you iterate. If reviewers request changes that alter behavior, revise the description to stay current.

Common Misconceptions

“The diff speaks for itself.”

Diffs explain what changed, not why. Business context, risk, and validation rarely live in code.

“I’ll write docs later.”

PR time is the cheapest moment for documentation. Future you will thank present you.

“Data PRs are the same as app PRs.”

Data changes propagate to dashboards and ML models. Impact analysis and backfill strategies are unique to data workflows.

Real-World Example

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`.


Galaxy & Pull-Requests

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.

Actionable Checklist

• [ ] Clear title summarising change
• [ ] Context references issue/ticket
• [ ] Bullet list of changes
• [ ] Downstream impact enumerated
• [ ] Validation steps reproducible
• [ ] Rollback or feature flag plan
• [ ] Reviewer guidance provided

Key Takeaways

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.