Why Your Data Team Keeps Investigating the Same Pipeline Failures

Data pipeline failures are inevitable. Repeatedly investigating the same failures is a different problem.

For UK companies running Snowflake, dbt and modern data platforms, recurring failures can consume valuable engineering time. A failed dbt test delays reporting. An upstream schema change breaks downstream models. A slow Snowflake query pushes costs higher. An ingestion job fails overnight, leaving the team to work out what happened the next morning.

The immediate fix is often straightforward. The underlying cause can be harder to identify.

The real cost of unreliable data pipelines

When a pipeline fails, engineers need to establish:

• Which job or model failed, and when?
• Was the failure caused by bad source data, a schema change, a transformation or an infrastructure issue?
• Which downstream models, reports or consumers are affected?
• Has this happened before?
• What needs to change to prevent a recurrence?

Without reliable logs, clear dependencies and useful failure evidence, engineers can spend hours tracing problems across jobs, SQL models, orchestration tools and source systems.

That investigation time adds up. It also takes experienced engineers away from planned platform improvements.

Why fixing the error is not always enough

Consider a dbt model that repeatedly fails a uniqueness test.

The team might remove duplicate records, rerun the model and get the pipeline green again. But where did the duplicates originate?

Possible causes include a source system change, an ingestion process replaying records, an incorrect join or a change in the model's assumptions.

Fixing the immediate symptom without identifying the cause leaves the system vulnerable to the next failure.

The same applies to expensive Snowflake queries. Increasing warehouse size might reduce execution time, but it may not address inefficient SQL, unnecessary data processing or poor workload scheduling.

Reliable data engineering means understanding why something failed, not just getting it running again.

A structured approach to data reliability

A useful investigation starts with evidence rather than guesswork.

1. Establish the failure

Collect relevant dbt test results, job logs, query history, execution timestamps and error messages.

2. Trace dependencies

Identify upstream sources, intermediate transformations and downstream models that may be affected.

3. Find the underlying cause

Separate the immediate error from the underlying issue. Check whether the failure is isolated or part of a recurring pattern.

4. Recommend a verified fix

Prioritise changes based on impact, recurrence and risk. Validate proposed fixes in an appropriate environment before deploying them.

5. Prevent recurrence

Where justified, improve tests, monitoring, alerting, dependency handling or operational documentation.

Not every failure needs a new tool. Sometimes the best solution is a small change to a model, a missing test or a better operational procedure.

When should you bring in external engineering support?

Internal teams should own their data platforms. But there are situations where additional engineering capacity can help.

For example:

• Your team repeatedly investigates similar dbt failures.
• Pipeline incidents interrupt planned engineering work.
• Upstream changes regularly break downstream transformations.
• Snowflake queries are becoming slower or more expensive.
• Your team lacks time to investigate recurring problems properly.
• You need an independent technical review before committing to a larger remediation project.

External support is most useful when it produces a clear diagnosis, evidence-backed recommendations and a practical path to resolution, rather than simply adding another person to the incident queue.

How MargallaAI can help

MargallaAI provides Snowflake, dbt and data engineering support for teams that need help investigating production data problems.

A focused diagnostic engagement can examine a specific recurring failure, a group of failing dbt tests, an upstream dependency issue or a Snowflake performance problem.

The scope is agreed in advance. The objective is to identify the likely root cause, document the supporting evidence, prioritise remediation and recommend steps to reduce repeat incidents.

Access requirements and security controls are agreed before any investigation begins. Where production access is not appropriate, the initial review can use redacted logs, query plans, test results and other supplied evidence.

The aim is straightforward: help your engineers spend less time repeatedly diagnosing the same problems and more time improving the data platform.

Dealing with recurring pipeline failures or dbt test issues?

Contact MargallaAI to discuss a focused data reliability investigation.