Data Intelligence
AI Data Engineer
The pipeline that makes models work in production
Builds and maintains the pipelines that feed AI systems. Ingestion from source systems, transformation into usable formats, orchestration, monitoring. Ensures the data arrives on time, correctly shaped, and trustworthy.
What this role covers
Pipeline engineering — Ingestion, transformation, orchestration, freshness monitoring
Data quality — Schemas, contracts, anomaly detection, lineage tracking
Source system integration — Connecting databases, SaaS tools, event streams, and APIs
Orchestration — Scheduling, dependency management, failure recovery
Freshness guarantees — Real-time and near-real-time data delivery for live AI systems
When you need this role
Data-rich companies with poor pipeline maturity
"We have mountains of data and our AI models are trained on whatever someone could export to a CSV six months ago. Nothing is live."
Companies scaling from manual to automated operations
"We're trying to automate decisions that require a human to pull data from five different systems. We need the data unified before AI can touch it."