What does a data engineer do on an AI team?
The job is plumbing for models. You write pipelines that pull raw data from apps, logs and databases, clean it, and land it in warehouse or lakehouse tables. You schedule those pipelines, watch them, and fix them when an upstream source changes shape. Databricks lays out the full loop in its docs: pipelines ingest raw data, create feature tables, train models and run batch inference, then feed serving endpoints and monitoring.
On an AI team, the job reaches into two places a classic analytics data engineer rarely goes. The first is feature stores, which hold the model inputs used in training and serving. The second is vector indexes, which hold embeddings so a retrieval system can find documents similar to a query. Both have to stay in sync with the source tables you already own.
Why AI projects depend on this work
A model is only as good as the rows it learned from. Two failures come up again and again. Training/serving skew happens when a feature gets computed one way during training and another way in production. Leakage happens when a training row includes information from after the moment you are trying to predict. Databricks lists both problems in its Feature Store docs. It offers point-in-time joins, which rebuild feature values as they stood when each label was recorded, and it computes features the same way at inference as in training.
Retrieval systems have the same dependency. Databricks says its AI Search builds an index from a source table and can sync it automatically when that table changes. If the source table is stale or dirty, the chatbot answers from stale or dirty text. BLS makes the same point at the level of the whole occupation: as organizations adopt AI, database architects will be critical to sound design, backup and security.
Tools named in official documentation
For orchestration, AWS describes Apache Airflow as an open-source tool for creating, scheduling and monitoring workflows. Amazon Managed Workflows for Apache Airflow (MWAA) runs it as a managed service, so you write Python workflows without running the servers yourself.
On Databricks, the Feature Store lives in Unity Catalog. You can author features two ways. Feature Views are declarative, and Databricks runs the pipelines for you. Feature tables are Delta tables with a primary key, and you populate them yourself. Features publish to an Online Feature Store for real-time serving. Databricks AI Search, formerly Vector Search, indexes Delta Lake tables, streaming tables, materialized views or Iceberg v3 managed tables. It supports hybrid keyword and similarity search. Product names change often, so learn the ideas behind them as well as the tool names.
How the job differs from a data scientist
BLS lists a data scientist's duties as choosing useful data, building and validating models, presenting findings with visualizations, and making business recommendations. Data scientists clean data too, but mostly to answer their own question. A data engineer builds the shared, repeatable system that every scientist and model draws from. That means schemas, scheduling, access control, lineage, backfills and monitoring.
In practice the work meets at the feature table. The scientist decides which signals matter. The engineer makes those signals reliable at scale, correct at each point in time, and available in milliseconds when the model is live. If you would rather ship systems than run experiments, data engineering is probably the better fit.
Pay
USD a year, before tax. US figures. The BLS rows use the OEWS May 2025 database architects series, moved to 2026 with the Employment Cost Index. BLS has no data engineer series, so this is the nearest match and covers the whole occupation. The job ad rows are midpoints of US dollar pay ranges in 31 live AutomationRoles.ai data engineer ads from 26 employers on 5 November 2026. These ads lean toward large AI and technology employers and show base pay, without stock or bonus. Refer to the Data & AI Engineers salary guide for figures from live adverts.
What you need
- SQL and data modelingBLS says database architects need to understand database languages such as SQL. Designing tables that models and analysts can trust is the core skill.
- A programming languageBLS notes that database workers need to learn whatever programming language their employer uses. AWS docs show Airflow workflows written in Python.
- Pipeline orchestrationYou schedule, monitor and rerun jobs. Apache Airflow is one tool named in AWS documentation for this.
- Feature and vector store basicsKnow point-in-time joins, online and offline stores, embeddings, and how an index stays in sync with its source table.
- EducationBLS says database architects typically need a bachelor's degree in computer and information technology or a related field. That describes the whole occupation, not AI teams in particular.
- Certifications (optional)BLS says vendors and vendor-neutral providers offer certifications, and some employers require them for the products they use. A certification shows familiarity with a platform. It does not replace a record of shipped pipelines.
How to get there
- StartLearn SQL and one programming languageQuery, join and model data well before you touch AI tooling.
- EarlyBuild a scheduled pipelineIngest a real data source, clean it, load it into tables, and schedule it with an orchestrator such as Airflow.
- First roleTake a data or analytics engineering roleOwn production pipelines, data quality checks, backfills and access control.
- Next stepMove closer to modelsBuild feature tables with point-in-time correctness, and source tables for vector indexes used in retrieval.
- SeniorGrow toward platform or architecture workBLS says experienced database workers may advance to architect roles or to computer and information systems manager.
Open now on AutomationRoles.ai
Questions people ask
Do data engineers need a license to work on AI teams?
No. No license exists for this work in the United States, and no regulator oversees the job. Some employers ask for vendor certifications in the platforms they run.
Is a data engineer the same as a data scientist?
No. BLS describes data scientists as people who build and validate models and turn analysis into business recommendations. Data engineers build the pipelines, tables and serving systems that those models depend on.
What is a feature store?
Databricks describes its Feature Store as a central registry for the features used in AI and ML models, with governance, lineage and point-in-time joins. It serves the same feature logic in training and at inference, which prevents training/serving skew.
What does a data engineer do for RAG or chatbot projects?
They prepare and maintain the source tables that a vector index is built from, and they keep the index in sync as data changes. Databricks AI Search, for example, builds indexes from Delta Lake or Iceberg v3 tables and can sync automatically.
How much do data engineers earn?
Moved to 2026, BLS pay for database architects, the nearest series, has a US median of $143,900. That series covers the whole occupation, not AI specialists alone. Live US job ads on AutomationRoles.ai show a median base midpoint of $195,000, but they lean toward large tech employers and leave out stock and bonus.
Is demand for this work growing?
BLS projects database architect employment to grow 9 percent from 2025 to 2035. It links that growth to organizations adopting AI and needing quality data infrastructure.
Sources
- U.S. Bureau of Labor Statistics: Database Administrators and Architects, Occupational Outlook Handbook
- U.S. Bureau of Labor Statistics: Data Scientists, Occupational Outlook Handbook
- Databricks documentation: Databricks Feature Store
- Databricks documentation: Databricks AI Search (formerly Vector Search)
- AWS documentation: What Is Amazon Managed Workflows for Apache Airflow?
