Quality data demands quality code. In the rush to harness data for insights, many organizations fixate on data cleansing and accuracy but overlook the engine behind it — the code. The result? Even “clean” data can lead to poor outcomes if generated by buggy or inefficient code.

Data Quality Engineering(DQE) flips this script by making code quality a first-class citizen in data projects. It’s a mindset shift and a toolkit of practices ensuring that the pipelines delivering your data are as robust as the data itself.

Why Does This Matter?

According to a McKinsey Digital survey, 82% of companies spend at least one day every week resolving data quality issues, often manually. Without DQE, teams chase errors reactively. Worse, a minor code bug in a retail inventory model can cascade into empty shelves or overstocked warehouses, costing sales and reputation.

In short, quality data is useless if the code can’t be trusted. DQE addresses this by baking quality into code from the start, so issues are prevented rather than patched.

What Does DQE Look Like in Practice?

DQE isn’t just a buzzword — it’s implemented through concrete engineering practices:

• Shift-Left Testing in CI/CD: Quality checks move earlier in the development cycle. Instead of discovering a critical flaw in production (where a fix might cost 100x more), developers catch it in design or build phases.
• Automated Testing and Monitoring: DQE encourages pervasive automated unit tests, integration tests, and data validation across the pipeline. Automated job monitoring and alerting replace the old “check the logs” approach — machines detect failures in real-time.
• Behavior-Driven & Test-Driven Development (BDD/TDD): DQE culture treats testing as a design activity, not an afterthought. Teams write tests to describe desired behavior before writing the code that implements it.
• DataOps and Cross-Team Collaboration: High-quality data systems require tight collaboration between data engineers, data scientists, QA, and business stakeholders. DQE aligns with DataOps principles — agile and DevOps philosophies adapted to data.

In the wild, what impact does DQE have? A recent initiative at an enterprise demonstrated the value: by introducing automated testing and observability on their Azure Databricks platform, the team saved ~20 hours of manual work per week and is projected to achieve over 200% ROI in 3 years. DQE turned a reactive maintenance slog into a proactive improvement cycle.

Modern Tools and Frameworks for DQE

Practicing DQE isn’t just about process — it leverages tech tools that embed quality into data pipelines. Here are some of the notable approaches and tools, and how they compare:

(GX = Great Expectations; LDP = Lakeflow Declarative Pipelines; DLT = Delta Live Tables)

Strong Opinions: Our POV on Driving Code Quality

Making a blog post “opinionated” means we don’t shy away from clear recommendations. So, here’s ours:

• Start with DQE as a mindset, not a tool. If your organization treats data pipeline code as throwaway scripts, it’s time to change. Invest in code quality like it is product code — because it is. That means planning refactoring to tackle technical debt (those ancient spaghetti SQL scripts aren’t benign — they’re ticking time bombs).
• Automate anything that humans do more than twice. Got a support engineer who spends every morning eyeballing pipeline run logs? That’s a prime candidate for automation. Build health checks into the pipeline, use cloud monitoring tools, set up alerts — don’t rely on heroics.
• Use the right tool for the job but keep the tool count minimal. There’s a temptation to implement every shiny tool at once — resist it. Pick a primary data quality enforcement approach and supplement it as needed.
• Measure and celebrate quality improvements. Track metrics like defect escape rate, pipeline recovery time, or hours of manual effort saved. DQE’s benefits can be quantified — reduced incident counts, faster releases, or higher stakeholder trust in data.

Finally, keep an eye on emerging trends: data observability platforms, AI-assisted testing, and “hyper-automation” of development are all converging with DQE. Future tools may be even smarter— AI that scans your code for anti-patterns or auto-generates test cases for data pipelines. But the foundation remains the same: a culture of quality and the smart application of tools.

Conclusion

Data Quality Engineering makes code quality a strategic asset. It’s not just about preventing disasters (though it does that); it’s about enabling trust and agility. When your data team isn’t constantly scrambling to fix broken pipelines, they can deliver new features faster. When business users know the data is right, they use it more, amplifying its value.

At Mphasis, we’ve seen first-hand that embracing DQE can transform data initiatives. In one engagement, instituting DQE practices turned a reactive maintenance project into a proactive improvement engine, yielding an estimated 300–500% ROI via saved effort and reduced errors. Those are real outcomes — more time for innovation, less spend on rework, and happier customers who aren’t disrupted by data mistakes. Learn more about Mphasis Next-gen Data Services.

In today’s data-driven world, basic blogs and basic approaches won’t cut it. Our strong point of view: if you’re serious about data, be serious about code quality. Integrate testing from the get-go, automate relentlessly, pick tools like GX, LDP/DLT, or DQX that fit your ecosystem, and measure your gains.

So, ask yourself: Are we treating data pipeline code with the respect it deserves? If not, it’s time to join the DQE movement — your data (and your users) will thank you.

Please note the opinions above are the author’s own and not necessarily my employer’s opinion. This blog article is intended to generate discussion and dialogue with the audience. If I have inadvertently hurt your feelings in any way, then I’m sorry.