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Data Engineer (AI-focused)

Build the pipelines that feed every model.

You design, build, and maintain the data infrastructure that ML and AI systems depend on. Pipelines, vector stores, data quality, lineage. Without you the model is just a math problem with no inputs.

Start the recommended pathCompare other roles

Skills profile

What hiring managers actually weigh.

Recommended paths

Take them in order. Each builds on the previous.

1

AI Engineering Foundations

The shortest path from software engineer to AI engineer.

2

RAG & Vector Databases

Make models answer from your data, not their guesses.

3

Deployment & MLOps

Get your model off the laptop and into production.

90-day roadmap

An honest, week-by-week plan. Adjust to your weekly time budget.

  1. 1

    Foundations

    ~4 weeks
    • Python + SQL fluency
    • Build a tiny ETL pipeline
    • Understand columnar storage
  2. 2

    AI-specific data

    ~5 weeks
    • RAG pipeline (chunk + embed + index)
    • Eval data quality
    • Schema for ML features
  3. 3

    Production

    ~4 weeks
    • Cloud platform of choice
    • Observability + lineage
    • Cost & SLA budgets

Certifications worth taking

Practice & certify on CertQuests after completing the course.

Career outcomes

Realistic next steps after completing the roadmap.

  • Pivot from BI / analytics → DE
  • Senior DE in AI-first companies
  • Foundation for MLE