Hiring nowData engineering for AI

Analytics Engineer for AI Products

Also called AI Analytics Engineer

An Analytics Engineer for AI Products builds the trusted tables and metric definitions that show how models are used and what outcomes they produce. It lets a company make product and model decisions from numbers everyone agrees on.

What they do

They take raw events from model calls, user sessions and feedback, and turn them into clean, tested analytical models. They write the SQL and Python that define metrics such as usage, cost per task, resolution and retention, and they document each one. They build and schedule pipelines, add tests for freshness and correctness, and trace figures back to source events. They work with product, data science and engineering to agree what a metric means before it appears on a dashboard. When numbers disagree, they find out why and fix it at the source.

What a strong hire delivers

In the first months, a strong hire delivers a small set of core metric definitions that product, finance and engineering all accept. They put tested analytics tables in place for model usage and outcomes. They set up reconciliation checks against raw events, so drift is caught early. They also leave clear documentation, so others can build on the work without asking them.

How you can tell they are good

Ask them to walk through a metric they defined and how they proved it was right. Strong people talk about grain, edge cases, late events and reconciliation, not just dashboards. Ask how they handled two teams who defined the same metric differently. Look for someone who tests their own work and can explain a past error plainly.

Why it is hard to fill

The title is new and rarely used the same way twice. Most candidates are analytics engineers with no exposure to model data, or data engineers who have never owned metric definitions. The mix is rare: strong modelling and semantic layer judgement, plus comfort with messy event data from AI applications. Many good people sit under other titles, so searching on the name alone misses them.

Where to find them

Look at analytics engineers from product-led software companies, and data engineers who have worked close to product analytics. Backend engineers who built event tracking and then moved into data are another source. Companies running consumer apps, developer platforms or marketplaces with heavy event data tend to produce people who think this way. Teams at AI application companies are also worth a look.

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 Analytics Engineer for AI Products

  1. Tell me about a metric you defined from raw events. How did you show it was correct?

    A strong answer covers grain, definitions agreed with stakeholders, tests, and reconciliation back to raw events. Worry if they only describe building a dashboard or cannot say how they checked the numbers.

  2. Two teams report different usage figures for the same model feature. How do you investigate?

    Good answers start by comparing definitions and filters, then trace both back to source events, checking duplicates, late data and joins. Worry if they pick a winner without tracing the cause.

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

Job description you can post

We are building AI products and need an Analytics Engineer to make our data on model usage and outcomes trustworthy. You will own the tables and metric definitions the whole company relies on, and work closely with product, data science and engineering.

What you will do

  • Build and maintain tested analytics tables for model usage and outcomes
  • Define and document core metrics with product, finance and engineering
  • Reconcile analytics figures to raw events and investigate gaps
  • Develop and schedule data pipelines for event and model data
  • Maintain a semantic layer so metrics stay consistent across tools
  • Add data quality tests and monitor freshness and correctness

What you will bring

  • Strong SQL, shown through analytical models you have built and owned
  • Working Python for pipelines and tooling
  • Experience with orchestration and transformation tools such as Airflow and dbt
  • Evidence of defining metrics and resolving disagreements over them
  • Understanding of data quality, lineage and provenance
  • Comfort with large event datasets and distributed processing

Nice to have

  • Experience with data from model or agent-based applications
  • Experience building a semantic layer
  • Familiarity with Spark

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…EmergingAI Data Reliability EngineerAn AI Data Reliability Engineer finds and fixes failures in the data that feeds AI systems. Bad data quietly degrades models and products, so this…

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 Analytics Engineer for AI Products?

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.