Data Reliability Engineering - Evolving Roles in Data Domain
“As challenges in data evolve, so do the roles that tackle them."
Data Reliability Engineering – The Why, What, and How
We’ve all heard of Data Engineering. It’s the discipline that builds the pipelines, moves the data from point A to point B, transforms it into usable formats, and stores it where analysts, data scientists, and applications can get to it.
For years, that was enough. If the pipeline worked, and the data landed where it should, the job was done.
But the world has changed. Businesses today don’t just use data — they depend on it.
Product decisions, operational strategies, customer experiences, AI models — all are powered by data. And when that data is late, wrong, or incomplete, it’s not just a minor inconvenience anymore; it’s a direct hit to the business.
This is where Data Reliability Engineering (DRE) comes in.
From Data Engineering to Data Reliability Engineering
Data Engineering gave us the modern data stack: ingestion systems, transformation pipelines, warehouses, data lakes, and analytics platforms. It solved the how do we move and store huge amounts of data problem.
But as companies became data-first, a new challenge emerged:
It’s not enough to have the data — it has to be reliable.
Reliable means:
- It arrives on time (freshness).
- It’s complete (no missing chunks).
- It’s correct (matches business definitions).
- It’s accessible (users can query it without issues).
- It’s consistent over time (no sudden, unexplained changes).
When these expectations aren’t met, you see things like:
- Dashboards showing wrong numbers.
- Machine learning models making bad predictions.
- Reports that don’t match each other.
- Teams making decisions based on outdated information.
And so, the role of Data Reliability Engineering was born — taking lessons from Site Reliability Engineering and applying them to data systems.
Why Bring SRE Principles Into Data?
Site Reliability Engineering (SRE) was pioneered at Google in the early 2000s by Benjamin Treynor Sloss to make massive, complex software systems more reliable. It introduced practices like:
- SLIs/SLOs — Service Level Indicators/Objectives that quantify what “good” looks like.
- Error budgets — Deciding how much unreliability you can tolerate before slowing down changes.
- Automating away manual fixes — Reducing human error and speeding recovery.
- Observability — Collecting enough metrics, logs, and traces to understand system health in real time.
These concepts made websites and applications more resilient. But data systems share the same issues — just in a different form. Instead of downtime, you might have stale data. Instead of application bugs, you have schema changes that break reports.
So, the leap was obvious:
If our data is just as critical as our applications, why not manage it with the same rigor?
Who First Used the Term “DRE”?
There’s no single person who can claim to have “invented” DRE. But around 2020–2021, forward-thinking data teams started using the term:
- Weave Lab described it as “applying SRE principles to data systems.”
- Dayforce positioned DRE as “SRE for Machine Learning and Data platforms.”
- Vendors in the data observability space began using the phrase to describe the evolution from simple data quality checks to end-to-end reliability practices.
What’s clear is that it emerged organically — different companies realized the same problem at the same time and reached for the same solution.
The Role of a Data Reliability Engineer Across the Data Pipeline
Just like with Data Governance, the responsibilities of a DRE span the entire pipeline.
1. Ingestion
- Define SLIs/SLOs for data freshness and delivery times.
- Monitor ingestion jobs for failures, delays, and partial loads.
- Automate retries, backfills, and incident notifications.
2. Raw Storage
- Check that the data is complete and matches source counts.
- Detect duplicates, corruption, or unexpected gaps.
- Apply retention and archival rules.
3. Transformation
- Validate schema changes before they hit production.
- Catch transformations that produce unexpected values.
- Log all changes for traceability and debugging.
4. Curated / Trusted Zone
- Enforce quality thresholds before certifying datasets.
- Ensure business rules are consistently applied.
- Mask or anonymize sensitive data to meet compliance.
5. Consumption
- Monitor usage patterns for anomalies (e.g., a sudden drop in queries might mean something broke upstream).
- Alert consumers proactively when upstream data is delayed or wrong.
- Keep a record of SLIs/SLOs for downstream service expectations.
6. Continuous Monitoring
- Maintain dashboards showing data health metrics.
- Track error budgets and reliability trends.
- Conduct postmortems after incidents to prevent recurrence.
How Data Governance Fits Into the Picture
Think of it this way:
- Data Governance defines the rules — what quality means, how data is classified, who owns it, how long it should be kept.
- Data Reliability Engineering makes sure those rules are enforced in practice and that data consistently meets those standards.
Without governance, DRE has no target to aim for.
Without DRE, governance remains a policy document that no one enforces.
Other Related Terms You’ll Hear
- DataOps — A broader operational philosophy covering governance, monitoring, automation, and delivery of data products.
- Data Observability — The tooling and practices used to detect and diagnose data issues.
- Data Quality Engineering — Focused on correctness, completeness, and validation testing.
The Bottom Line
We’ve moved from an era where getting the data was the main challenge to an era where trusting the data is the challenge.
Data Reliability Engineering is the answer — blending governance policies, SRE discipline, and modern automation to ensure data is always available, accurate, and fit for purpose.
Because in today’s world, bad data is just as dangerous as no data.