ABSTRACT: Building trust in data starts with engineering. This article explores how teams can embed quality into every layer of the pipeline—from ingestion to output.

What You’ll Learn:

• Why data quality challenges differ across application and analytics teams

• How to apply engineering principles that improve reliability across pipeline stages

• How to design pipelines for testability, observability, and reusability

• Where and how to embed stage-specific data quality checks (from Bronze to Output)

• How modern data platforms support quality enforcement through modular plug-ins

How application teams and analytics teams approach data quality is often very different. For application owners, the data might look fine, but for those building BI dashboards or machine learning models, it may be far from usable. That misalignment can lead to endless downstream rework, mistrust in the numbers, and major inefficiencies.

In our previous article, Getting Data Quality Right: A Tactical and Strategic Guide, we explored how data quality problems often stem from broken processes—not broken data—and how tactical stabilization must work alongside long-term strategy. That article introduced a programmatic view of Data Quality Management (DQM) and the dual-track journey to improvement.

In this follow-up, we shift gears to the engineering side of the equation. Drawing from Carlos Bossy’s session in the webinar How to Handle Nasty, Gnarly, and Pernicious Data Quality Issues, we explore how quality can (and should) be embedded into the data pipeline itself—from ingestion to output.

Why Engineering Needs a Quality Mindset

According to Datalere, one of the biggest gaps in how data pipelines are built today is the absence of proactive quality thinking. Unlike application developers, who often use test-first methodologies, many data teams begin building pipelines without a clear plan for how they’ll validate outputs, detect issues, or accommodate inevitable changes in structure and input.

That’s why we recommend a shift in mindset:

• Think test-first. Start with test planning just like application developers do. Write unit tests, define assertions, and mock sample inputs before writing transformations. This mindset reduces debugging effort downstream and ensures expected outcomes from the get-go.

• Expect bad data. Design your pipeline to route, quarantine, or clean problematic data without halting execution. Set up alerting thresholds for issues such as null spikes, duplicate records, or values outside expected ranges.

• Plan for schema changes. Data sources change frequently, with new fields, renamed columns, and missing values. Automate schema validation at ingestion and configure alerts for any structural deviation.

• Establish foundational elements early. Set up basic infrastructure, including configurable parameters, pipeline metadata tracking, retry logic, and logging, before scaling out. These make pipelines easier to manage, especially in production.

Engineering Practices That Improve Quality

Strong pipelines are built on strong engineering habits. We recommend adopting time-tested principles from software development to raise the bar on reliability. These practices—like modular coding, version control, automated testing, and continuous integration—bring much-needed structure to an environment that’s often ad hoc and reactive.

With the right foundations in place, pipelines become easier to test, maintain, and scale, improving not just data quality, but the entire delivery process.

• Borrow from app dev. Apply software engineering practices: use modular functions, version control (like Git), CI/CD pipelines for deployment, and test-driven development (TDD) where possible.

• Manage exceptions. Instead of letting pipelines crash, trap errors, and write logs with detailed context. Flag anomalies but allow partial success where possible to keep systems running while alerting engineering teams.

• Use assertions and logging. Assert that data matches expected patterns—row counts, key uniqueness, acceptable ranges. Log not just failures, but also indicators of slow drift over time.

• Do code reviews. Peer reviews help catch logic errors, improve code quality, and share domain knowledge. Even small tweaks can have big downstream effects, so review is essential.

• Track DQ through tickets. Establish a backlog of known data quality issues and use that to measure progress. Carlos mentioned using the number of open data tickets as a live indicator of quality trends.

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Quality in Practice: Across Every Stage of the Pipeline

Each stage of the data pipeline serves a different purpose—and requires its own definition of quality. That’s why leading teams design checks into every layer, not just the final output. From ingestion to transformation to delivery, quality needs to be measured, validated, and preserved throughout.

Embedding quality at each stage helps detect errors early, maintain trust across handoffs, and ensure that data remains fit for purpose all the way through.

• Bronze (Raw Ingested Data)
  Purpose: Ensure nothing is missing or malformed as data enters the system.
  → Data Quality Check: Validate record count, file completeness, and schema conformance. Flag gaps in ingestion or malformed source files early, before they contaminate downstream processes.

• Silver (Cleansed and Transformed Data)
  Purpose: Standardize structure and apply critical business logic.
  → Data Quality Check: Enforce data types, check join integrity across tables, monitor for nulls in mandatory fields, and detect duplicates. These checks protect the reliability of shared reference data.

• Gold (Curated for Analytics)
  Purpose: Align data to analytical models and business KPIs.
  → Data Quality Check: Run metric validations—e.g., does revenue per customer match past patterns? Are aggregates within thresholds? These checks help spot broken logic or subtle transformations gone wrong.

• Semantic Layer
  Purpose: Ensure consistency in business definitions across reports and dashboards.
  → Data Quality Check: Validate alignment with naming conventions, metric logic, and data contracts. Spot discrepancies where metrics like “customer churn” or “active user” differ across teams or tools.

• Output (Dashboards, APIs, Models)
  Purpose: Confirm the data delivered to stakeholders is fresh, accurate, and complete.
  → Data Quality Check: Track data freshness, error rates in pipelines, failed API calls, and user-facing anomalies. These checks protect the credibility of final insights with consumers.

The Role of Tools and Reusability

Modern data platforms often include built-in modules for validation, monitoring, and schema checks. Rather than building everything from scratch, teams can plug these components into their existing stack, reducing engineering effort while improving transparency. The goal is to provide both technical and business users with clear visibility into how quality is managed across the pipeline.

In some environments, performing data quality checks directly within the data catalog may not be practical—especially where access to sensitive data is restricted. In such cases, what’s needed is not raw data access but visibility into metadata, definitions, and lineage. This allows teams to monitor and manage quality without compromising security or governance.

Below is a sample list of data quality platforms and tools. These are not recommendations or endorsements—just examples of what's available across different categories:

Conclusion: Build Trust into the Pipeline

Engineering teams today have the opportunity to do more than just move and transform data—they can build trust into every layer of the data pipeline. As Carlos Bossy emphasized during the webinar, quality begins with design: the small, intentional choices that shape how data flows, how issues are detected, and how teams respond. With reusable components, early validation, and quality checks at every stage, data pipelines can evolve from fragile workflows into resilient systems that scale with the business and earn confidence with every delivery.

Key Takeaways

• Data engineers must consider quality from the outset, not as an afterthought.

• Borrowing from app development practices helps create more robust, testable pipelines.

• Quality checks should be embedded across every pipeline stage, tailored to the function of each.

• Expect bad data and schema changes, and build resilient systems to handle them.

• Observability and modularity are critical to maintaining quality at scale.

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