Hiring nowData engineering for AI

AI Data Engineer

Also called ML Data Engineer

An AI Data Engineer builds the pipelines that feed model training and evaluation, with clear lineage back to source. Without them, teams cannot trust their results or reproduce a run when something goes wrong.

What they do

They build and run ingestion and transformation pipelines for training and evaluation data. They pull from product databases, event streams, files and third-party sources, then clean, join and version the output. They design schemas that can change without breaking downstream jobs. They choose between batch and stream processing for each flow. They add checks for freshness, completeness and defects, and they record lineage so any dataset can be traced to its inputs and code. They work closely with ML engineers and researchers, who tell them what the data must look like, and they fix problems when a run fails or numbers drift.

What a strong hire delivers

In the first months, a strong hire gives the team a reproducible pipeline for at least one important dataset, with versioned outputs and recorded lineage. Data quality checks run automatically and alert the right people. Freshness and completeness are measured and visible. Researchers stop rebuilding data by hand, and the team can rerun an earlier experiment and get the same inputs.

How you can tell they are good

Ask them to walk through a pipeline they built and what broke in it. Strong candidates talk about schema changes, late or duplicate data, backfills and how they proved a dataset was correct. They can explain how they would trace a bad model result back to a data fault. Be wary of answers that only list tools and never mention failures or checks.

Why it is hard to fill

The title is used loosely. Some candidates are classic warehouse engineers with no exposure to model workflows. Others are ML engineers who write pipelines that work once and are hard to repeat. The hire needs both discipline in data engineering and a feel for what models need. Few people have shown both, so strong candidates are often found by adjacency and then checked carefully.

Where to find them

Most come from data engineering teams in product companies, where they ran large batch and streaming pipelines. Others move over from backend engineering, analytics engineering or platform teams. Some have worked beside ML teams in search, recommendation, fraud or advertising. Look for people who owned data quality and lineage, not only job scheduling. Growth-stage software companies and data-heavy businesses are good places to start.

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 Engineer

  1. Describe a pipeline you built that fed a model or analytics product. What went wrong in it, and what did you change?

    A strong answer names a real failure, such as duplicates, late data or a silent schema change, and the specific fix and monitoring added afterwards. Worry if they describe only the happy path or claim nothing ever broke.

  2. How do you handle a source system changing its schema without warning?

    Good answers cover contracts or validation at ingestion, tolerant parsing, versioned schemas, alerts and a plan for backfilling. They think about downstream consumers. Worry if the answer is to fix it by hand when someone complains.

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

Job description you can post

We are building products that depend on models, and those models are only as good as the data behind them. We are looking for an AI Data Engineer to build and run the pipelines that produce our training and evaluation data, with clear lineage and quality checks. You will work closely with our ML engineers and researchers.

What you will do

  • Build reproducible ingestion and transformation pipelines for training and evaluation data
  • Record lineage so every dataset can be traced to its sources and code
  • Design schemas and handle schema changes without breaking downstream work
  • Choose and run batch and stream processing for each data flow
  • Set up checks and alerts for freshness, completeness and defect rate
  • Work with ML engineers and researchers to define what the data must look like
  • Investigate and fix pipeline failures and data drift

What you will bring

  • Shipped data pipelines in production, with examples you can explain in detail
  • Strong SQL and Python
  • Experience with distributed processing and orchestration tools
  • Demonstrated work on data quality, testing and provenance
  • Experience handling schema evolution and backfills
  • Clear communication with both engineers and researchers

Nice to have

  • Experience supporting model training or evaluation workflows
  • Experience with dataset versioning
  • Background in backend or analytics engineering

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

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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.

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