Data reliability engineer
Ensures availability, integrity and predictable quality across data stores, pipelines and analytical products.
On this profession page, you will learn:
Who is Data reliability engineer
Ensures availability, integrity and predictable quality across data stores, pipelines and analytical products.The engineer applies reliability practice to data platforms by setting quality objectives, observing dependencies, managing incidents and preventing recurrence. For a data reliability engineer, data platform reliability supports the task “Build monitoring for pipelines, schemas and critical datasets” without substituting assumptions for verified facts.
AI impact on Data reliability engineer
Medium riskAI replacement risk
50%
For a data reliability engineer, AI can support data platform reliability and draft material for the task “Define SLI/SLO targets for freshness, completeness and accuracy”. For a data reliability engineer, human review of “Run root-cause reviews and implement preventive controls” must be grounded in data platform reliability.
Tasks at risk of automation
- Define SLI/SLO targets for freshness, completeness and accuracy.
- Build monitoring for pipelines, schemas and critical datasets.
Tasks that will remain human
- Lead response to data incidents and communicate impact.
- Run root-cause reviews and implement preventive controls.
Key skills of Data reliability engineer
Work schedule and conditions
Works in data-intensive companies alongside analysts, data engineers, platform engineers and product owners. Critical platforms may require on-call duty.
What a Data reliability engineer does
- Define SLI/SLO targets for freshness, completeness and accuracy.
- Build monitoring for pipelines, schemas and critical datasets.
- Lead response to data incidents and communicate impact.
- Run root-cause reviews and implement preventive controls.
Benefits of the Data reliability engineer profession
Professional reward
The work protects trust in data used for product, financial and operational decisions.
Cross-disciplinary work
Data platform reliability and data quality and observability grow through the task “Lead response to data incidents and communicate impact”.
Disadvantages of the Data reliability engineer profession
Work pressure
A failure may stay invisible until an incorrect number has already influenced a decision.
Demanding precision
In data reliability engineer work, data platform reliability and the task “Run root-cause reviews and implement preventive controls” shape the result and its documented limits.
Personal advice
No advice found
How to become a Data reliability engineer
Develop data platform reliability and data quality and observability, beginning with the task “Define SLI/SLO targets for freshness, completeness and accuracy”.
1. Study
Study computer science, data engineering or information systems. Learn SQL, orchestration, observability, statistical quality control and incident management.
2. Practice
Set freshness and completeness objectives for a sample pipeline, create alerts, simulate a late source and write a concise postmortem.
Data platform reliability can be demonstrated in a data reliability engineer portfolio through this assignment: Set freshness and completeness objectives for a sample pipeline, create alerts, simulate a late source and write a concise postmortem.
Questions and answers
No questions found