EmergingData engineering for AI

AI Data Reliability Engineer

Also called Data SRE, AI

An AI Data Reliability Engineer finds and fixes failures in the data that feeds AI systems. Bad data quietly degrades models and products, so this person protects quality before customers or model results suffer.

What they do

They watch the data flowing into models and AI applications and work out when something has gone wrong. That might be a late feed, a schema change upstream, a silent drop in volume or values that look valid but are wrong. They build monitoring and alerting on pipelines, agree data contracts with the teams who produce the data, and trace each incident back to its source. They write recovery procedures, run backfills and take part in on-call. After every incident they fix the cause, not just the symptom, and record what was learned.

What a strong hire delivers

In the first months, a strong hire gives the team clear visibility of the data feeding its AI systems. They set up monitoring on the most important pipelines, agree data contracts with upstream owners and write recovery procedures people can follow under pressure. Incidents get caught earlier, resolved faster and repeat less often. The team also learns which data sources it can trust and which it cannot.

How you can tell they are good

Ask them to walk through a real data incident from first alert to final fix. A strong candidate explains how they detected it, how they found the cause, what they did to recover and what they changed so it did not return. They talk about contracts, ownership and provenance, not just dashboards. Be wary of anyone who only describes adding more alerts.

Why it is hard to fill

The title barely exists yet, so you will rarely find it on a CV. The work is usually hidden inside data engineer, backend or analytics engineer jobs, and the best people have never been labelled for it. Candidates tend to be strong on pipelines but light on AI, or the reverse. Reliability instincts matter more than any keyword, and they take some effort to see in an interview.

Where to find them

Look at data engineers who end up owning on-call for pipelines, backend engineers who have run data-heavy services, and analytics engineers who built testing and quality checks. Site reliability and platform engineers who moved towards data are also worth a conversation. They tend to come from companies where bad data has a visible cost, such as marketplaces, fintech, and teams running machine learning in production.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for an AI Data Reliability Engineer

  1. Tell me about a data incident you handled end to end. How was it detected, and what changed afterwards?

    A strong answer covers detection, diagnosis, recovery and a lasting fix, with a clear sense of their own part. Worry if they only describe the firefight, blame others, or cannot say what stopped it recurring.

  2. How would you decide which pipelines feeding an AI system deserve monitoring first?

    Good answers rank by impact on model or product quality, data volatility and past failures. They start small and expand. Worry if they propose monitoring everything equally or never ask how the data is used.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We build AI products that depend on a steady supply of good data. We are hiring an AI Data Reliability Engineer to make sure that data is trustworthy. You will find problems early, fix them at the source and make recovery routine. You will work closely with data, engineering and machine learning colleagues.

What you will do

  • Build monitoring and alerting for the data pipelines that feed our models and applications
  • Agree and maintain data contracts with the teams that produce our data
  • Investigate data incidents and trace them to their root cause
  • Write, test and maintain recovery procedures, including safe backfills
  • Take part in on-call for critical data pipelines
  • Run reviews after incidents and follow through on fixes
  • Track how quickly we detect and recover from data defects, and how often they recur

What you will bring

  • Demonstrated work keeping data pipelines reliable in production
  • Strong SQL and Python
  • Experience with orchestration and transformation tools such as Airflow and dbt
  • Working knowledge of distributed processing and warehouse systems
  • A clear approach to data quality and provenance, with examples
  • Calm, methodical handling of incidents and clear written communication

Nice to have

  • Experience supporting machine learning or AI applications in production
  • Experience with Spark at scale
  • Experience introducing data contracts to a team that had none

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Data pipelinesAdvancedBuild reliable ingestion and transformation systems
  • SQLAdvancedQuery and validate relational data
  • Data quality and provenanceWorkingTrack dataset origin, validity and transformations
  • Distributed systemsWorkingDesign reliable work across networked machines

Other roles in data engineering for AI

Hiring nowAI Data EngineerAn AI Data Engineer builds the pipelines that feed model training and evaluation, with clear lineage back to source. Without them, teams cannot…Hiring nowTraining Data Infrastructure EngineerA Training Data Infrastructure Engineer builds the systems that feed datasets to model training at speed and scale. They keep every batch fast…Hiring nowAnalytics Engineer for AI ProductsAn Analytics Engineer for AI Products builds the trusted tables and metric definitions that show how models are used and what outcomes they produce…

Related roles in other families

Hiring nowKnowledge Graph EngineerKnowledge graphs and semantic systemsA Knowledge Graph Engineer builds linked records of the people, products and things a business cares about, each traced to its source. It gives AI…Hiring nowOntology EngineerKnowledge graphs and semantic systemsAn Ontology Engineer defines the concepts, relationships and rules that describe a business domain, so retrieval and AI systems return answers that…Hiring nowAI Knowledge ArchitectKnowledge graphs and semantic systemsAn AI Knowledge Architect designs how an application organises, governs and updates what it knows. That structure decides whether retrieval is…EmergingNeuro-Symbolic Knowledge EngineerKnowledge graphs and semantic systemsA neuro-symbolic knowledge engineer connects structured knowledge, such as ontologies and graphs, to learned models. The aim is answers that stay…

How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

← Data engineering for AIAll new job families

Start a search

Hiring an AI Data Reliability Engineer?

Tell us the work. We find the people already doing it, whatever their job title says, and you meet them already interviewed for your role.