On-Call That Doesn’t Suck: A Guide for Data Engineers

In large-scale data platforms, reliability doesn’t end with the pipeline's DAG finishing successfully. It ends when the data consumers, whether dashboards, ML models, or downstream pipelines, can trust the data. But ensuring this is harder than it sounds. Poorly designed alerts can turn on-call into a reactive firefight, masking the signal with noise and reducing operator effectiveness.

This article presents five engineering principles for scalable, actionable, and low-fatigue data quality monitoring systems, derived from real-world learnings.

Redefining Data Quality Beyond the Metrics

Data quality (DQ) is traditionally measured across six core dimensions: accuracy, completeness, timeliness, validity, uniqueness, and consistency. These definitions are foundational, but operational excellence in DQ comes from how these metrics are monitored and enforced in production.

When improperly scoped, even well-intentioned checks can contribute to operational overhead. Checks duplicated across layers, misaligned alert severities, and a lack of diagnostic context are common anti-patterns that can erode on-call effectiveness over time.

Principle 1: Establish Intent — Why Does This Alert Exist?

Each data quality check should serve a specific purpose and align with either an operational concern or a meaningful business outcome. Rather than treating validation as a routine step, it's important to evaluate whether a check provides new, relevant insight at its position in the pipeline.

For example, if upstream systems already verify schema structure or null value thresholds, repeating those same checks downstream can lead to redundant noise. Instead, downstream validation should focus on transformations, such as assessing the correctness of joins or the integrity of derived metrics. A well-placed check offers context-specific value and helps isolate issues where they are most likely to emerge. By eliminating duplication and narrowing the scope to critical validations, engineers can improve signal quality and reduce alert fatigue.

Principle 2: Own Scope — Where Should This Alert Live?

Alerting should be tightly aligned with the structure of the data pipeline. The most effective data quality checks are those placed at the point where they can provide the most relevant context, typically close to where data is ingested, transformed, or aggregated. When alerts are placed too far from the logic they monitor, it becomes difficult to pinpoint the root cause during incidents. This leads to slower resolution times and a heavier burden on on-call engineers.

To reduce ambiguity, each stage of the pipeline should be responsible for validating the assumptions it introduces. Ingestion layers are best suited for monitoring delivery completeness and freshness. Enrichment stages should validate schema evolution or type mismatches. Aggregation layers should verify logical correctness, such as deduplication, join integrity, or metric drift.

Data lineage tools are useful in this context; they help teams understand where alerts exist, identify overlaps, and ensure that no critical stage is left unmonitored. By aligning ownership and placement, alerting becomes not just more effective but also easier to maintain as systems evolve.

Principle 3: Quantify Severity — How Urgent Is This?

Not every anomaly requires the same level of operational response. A tiered severity model helps calibrate responses appropriately:

• Critical alerts should be reserved for events that require immediate attention, for example, a schema mismatch in a high-impact dataset or a logging regression that significantly affects metrics or causes data loss. These alerts should trigger a page.
• Warning-level alerts highlight degraded but non-critical conditions, such as a sudden rise in null values or a delay in a non-core pipeline. These are better suited for asynchronous channels like Slack or email, allowing engineers to respond during business hours.
• Informational alerts capture subtle shifts or trends, such as distribution changes in a shadow dataset, that may warrant monitoring but do not require action. These can be logged or visualized for periodic review.

Ideally, severity should be tied to service-level objectives (SLOs) or data SLAs. Over time, systems should be able to auto-escalate issues that persist or grow in impact, further reducing manual tuning and increasing alert fidelity.

Principle 4: Make It Actionable — What Should the Operator Do?

Alerts that lack diagnostic context add latency to incident resolution. Each alert should include not just a message but also relevant historical data, links to dashboards, and a clearly documented remediation path.

A well-structured alert should answer what changed, when it changed, what the potential impact is, and how to respond. Integrating dashboards that support historical comparisons, anomaly timelines, and impact estimation significantly improves mean time to resolution (MTTR).

Principle 5: Corroborate Signals — Can We Validate This Elsewhere?

High-quality alerting systems incorporate redundancy and cross-validation. Instead of relying solely on static thresholds, engineers should design mechanisms for comparing data streams across sources or over time.

Stream-to-stream comparisons, reference dataset verification, and statistical baseline monitoring are all effective strategies for identifying systemic shifts that individual checks may miss. For example, comparing Kafka ingestion volumes to downstream Flink output can reveal silent failures that static null checks might overlook.

Building Durable Systems Through Intentional Alerting

The benefits of a principled approach to alerting are tangible. When alerts are thoughtfully scoped, well-placed, and properly prioritized, teams experience fewer false positives, gain increased confidence in the stability of their pipelines, and resolve issues faster. Over time, this leads to a cultural shift, from reactive firefighting to proactive system stewardship. Engineering time moves away from triage toward continuous improvement, raising the overall reliability and trustworthiness of the data platform.

Looking ahead, the future of data quality monitoring lies in intelligent automation. Emerging approaches include the automatic placement of validation checks triggered by schema evolution, routing alerts based on data lineage and ownership, and applying real-time anomaly detection in streaming contexts. These techniques enable systems to adapt dynamically to shifts in behavior and usage, moving us closer to pipelines that are both self-aware and self-correcting.

Ultimately, building resilient data systems requires more than just correctness at the code or infrastructure level. It requires operational empathy, a recognition that maintainability, debuggability, and clear signaling are integral parts of system design. Teams that elevate alerting to a first-class engineering concern are better positioned to build platforms that not only function but also endure.

As the complexity of data ecosystems continues to grow, the core question becomes: are we building alerting systems that support engineers in making timely, informed decisions, or are we simply generating more noise? By investing in clarity, automation, and intentional design, we can ensure that our systems scale not only in size but also in trust.