The Complete Guide to Analytics PR Reviews: Beyond Just Passing Tests
Pull request reviews are one of the most critical—and often underutilized—practices in analytics engineering. While many teams focus solely on whether tests pass, effective PR reviews go much deeper, serving as the last line of defense for code quality, security, and maintainability.
At Brooklyn Data, we've learned that the difference between good and great analytics teams often comes down to their approach to code review. After working with dozens of clients and refining our own processes, we've developed a structured framework that transforms PR reviews from a checkbox exercise into a powerful tool for knowledge sharing and quality assurance.
Why Most PR Reviews Fall Short
The reality is that most PR reviews in analytics focus on the wrong things. Teams get caught up in style nitpicks or rely too heavily on automated tests, missing the critical issues that can compound over time:
- Security vulnerabilities that expose sensitive data
- Performance bottlenecks that slow down entire pipelines
- Inconsistent data modeling that creates technical debt
- Poor naming conventions that make code unmaintainable
The cost of catching these issues after deployment is exponentially higher than addressing them during review. Yet many reviewers don't know what to look for beyond "do the tests pass?"
What Makes an Effective Analytics PR Review
Our experience shows that the most impactful PR reviews focus on areas that are difficult to automate and expensive to fix later. Here's what skilled reviewers prioritize:
Code Quality & Architecture
Look for maintainable, scalable patterns rather than quick fixes. Ask yourself:
- Does this code follow established conventions?
- Will this be clear to someone six months from now?
- Are there opportunities to eliminate duplication?
Example: Instead of accepting a hardcoded date filter, suggest parameterizing it to make the model more flexible and maintainable.
Performance Impact
Analytics workloads can be resource-intensive, making performance considerations crucial: teams should evaluate both the immediate impact and the long-term scalability of their decisions.
- Will this change significantly impact query performance?
- Are there optimization opportunities using indexes or partitioning?
- Will the query pattern scale with data growth?
Security & Compliance
Data security should never be an afterthought. It must be embedded into every process and decision, not rushed in after systems are already built. By prioritizing protection from the start, organizations reduce risks, strengthen trust, and ensure long-term resilience. To make that commitment real, some critical questions to ask are:
- Is any sensitive data exposed inappropriately?
- Are proper access controls in place?
- Do data handling practices align with compliance requirements?
Data Consistency & Modeling
Consistency in data modeling prevents future headaches. When structures are predictable and standardized, teams can more easily collaborate, troubleshoot issues, and evolve systems without introducing errors. To ensure consistency, consider questions like:
- Do naming conventions align with established patterns?
- Are data types and constraints appropriate?
- Does this change maintain referential integrity?
What NOT to Focus On
Effective reviewers know when to let things slide. Avoid getting bogged down intrivial matters like:
- Style guide violations that should be caught by automated linters
- Semantic nitpicks that don't impact functionality
- Generic testing requirements without considering context
These areas are either better handled by tooling or don't meaningfully impact the project's success.
The Art of Constructive Feedback
How you communicate feedback matters as much as what you identify. Framing comments appropriately helps keep the process constructive and supportive. Here are the principles we follow:
Be Specific and Actionable
❌ "This could be better"
✅ "Consider using a CTE here to improve readability and make the logic easier to test"
Ask Questions Instead of Dictating
❌ "You need to change this approach"
✅ "What do you think about using a different approach here? I'm wondering if X might be more maintainable because..."
Reference Standards and Examples
Link to documentation, style guides, or previous implementations within the codebase. This not only helps contributors understand the reasoning behind your feedback, but also provides them with concrete resources to reference. By pointing to established standards and past examples, you reinforce consistency, reduce ambiguity, and create learning opportunities that strengthen future pull requests.
Balance Critical and Positive Feedback
Acknowledge good practices alongside areas for improvement. This reinforces positive patterns and makes critical feedback more palatable.
Teaching Clients to Review Effectively
Many of our clients are new to PR reviews, especially in analytics contexts. We've found success with this educational approach:
Start with the Fundamentals
Emphasize that PR review is about business logic, data accuracy, and maintainability, not just syntax. Help reviewers understand they're validating that the solution solves the right problem in the right way.
Provide Learning Resources
Share articles, documentation, and examples of effective reviews. The goal is to build internal capability, not create dependency on external reviewers.
Focus on High-Impact Areas
New reviewers can easily get overwhelmed. Start with the most critical areas:
- Data correctness and business logic
- Performance implications
- Security considerations
Improving Your PR Process
Well-structured templates guide contributors to think more critically about their changes and provide reviewers with the right context from the start. By standardizing expectations, you reduce back-and-forth, improve clarity, and ensure that important considerations aren’t overlooked.
Require contributors to address key areas upfront:
How to Test
Every PR should specify how changes have been validated, even if it's just running included unit tests.
Impact Assessment
For significant changes, document who will be affected and whether stakeholders have been informed.
Proof of Concept
Include screenshots, sample queries, or BI tool outputs when applicable to demonstrate the change works as intended.
Standardized Review Checklist
Consider implementing a checklist like this:
- ✅ Has the author tested performance impact?
- ✅ Have data consistency checks been performed?
- ✅ Are tests sufficient for the scope of the change?
- ✅ Are key design patterns and best practices followed?
- ✅ Is documentation clear and updated as needed?
- ✅ Have security implications been considered?
- ✅ Are changes aligned with business logic and stakeholder expectations?
Making Reviews Stick
The most well-intentioned PR review process fails if it's not sustainable. Here's how to make it stick:
Right-Size Your PRs
Encourage breaking down large PRs. Smaller, focused changes are easier to review thoroughly and less likely to introduce bugs.
Make It Educational
Use PR reviews as opportunities for knowledge transfer. Junior team members learn by participating in reviews, not just submitting them.
Iterate and Improve
Regularly assess your PR review process. What's working? What's causing friction? Adjust based on team feedback and outcomes.
Looking Ahead
Effective PR reviews are an investment in your team's long-term success. They catch issues early, spread knowledge across the team, and establish quality standards that compound over time.
The analytics engineering field is rapidly evolving, but the fundamentals of good code review remain constant: focus on what matters, communicate constructively, and use the process as an opportunity for growth.
As teams mature in their analytics engineering practices, those with strong review processes will build more maintainable systems, make fewer costly mistakes, and develop stronger technical capabilities across the organization. In our opinion, quality PR reviews don’t cost time, they save it.