AI Data Engineer. The role, the market signal, and how to build it in your org.
Builds the data layer AI runs on: ingestion, feature stores, embeddings, retrieval. The plumbing that makes agents and models usable on real enterprise data.
64 postings · 57 distinct titles · from 260,470 real job postings · see the live data →
What the postings ask this role to do
1,237 tasks extracted from real AI Data Engineer job descriptions, classified Automate / Augment / Human-only. Only 3.2% can be fully automated: companies are hiring this role for the judgment, not the keystrokes.
- Catalog datasets that support applied ai retraining workflows.
- Normalize collected data for downstream ai and data workflows.
- Document processes, workflows, and configurations for reference and future improvements.
- Develop ai-driven systems to improve data capabilities.
- Collect data from multiple sources, including external llm providers.
- Stay current with emerging tools and methodologies in ai data engineering.
- Develop graph database solutions for complex data relationships supporting ai systems.
- Design scalable real-time data pipelines that support ongoing applied ai model training.
- Mentor other members of the engineering community.
- Maintain clear communication channels across teams.
- Partner with ai r&d, applied ai, and data platform teams to ensure seamless data flow.
- Mentor junior team members and promote best practices, standards, and reusable patterns.
- Collaborate with cross-functional teams to integrate solutions into operational processes and systems.
From the market's version of this role to your version of it
Compose your org's AI Data Engineer job description
Start from the tasks real postings ask for, keep the ones that match your operation, add what is specific to you. The tasks carry their AI classification, so the JD you take away already says what AI runs and what stays with people.
Start with the work, not the org chart.
Run the audit on one operation and see what this role would own first.