Data Engineering Assessment — README template
Copy or adapt this into the data-engineering-assessment repo README. Replace placeholders in square brackets. Aligns with the AI challenge structure.
[Title: e.g. Data Engineering Technical Challenge]
Overview
This technical challenge evaluates a candidate’s ability to [one sentence: what the assessment asks them to do, e.g. design and implement a pipeline, model data, run queries]. It mirrors a realistic data engineering scenario.
Candidates are expected to [deliverable: e.g. complete the tasks in the repo and open a PR].
Objective
You will [2–4 bullet points: what they need to do].
- [Task 1]
- [Task 2]
- [Task 3]
Repository structure
[Describe key folders/files the candidate will use]
Requirements
Functional
- [What the solution must do / produce]
Technical
- [Tools they may use, e.g. DuckDB, dbt, Python]
- Include clear documentation and code comments
- A simple setup guide (e.g. requirements.txt, README run instructions)
- Optional: A short Loom (5–10 min) explaining your approach and design choices
Time expectation
- Expected effort: ~5 hours (or adjust).
- The challenge is intentionally open-ended — clarity and design choices are evaluated as much as the final output.
Submission instructions
- Fork this repository (you’ll receive access once selected for the challenge).
- Create a new branch named after yourself (e.g.
feature/jane-doe-solution). - Implement your solution within your branch.
- Submit a Pull Request to your fork when finished.
- In the PR description include: setup/run instructions, any assumptions, and (if required) Loom link.
- Share the fork link with the recruiting team.
Evaluation criteria
| Area | Description |
|---|---|
| Data / pipeline design | How well the solution handles data [ingestion / transformation / storage] and fits the problem. |
| Code quality | Structure, readability, maintainability, and adherence to common DE practices. |
| System design | Logical architecture, clarity of choices, and ability to generalize or extend. |
| Completeness | Meets submission and documentation requirements; run/validation succeeds. |
| Presentation | If Loom required: clarity and professionalism of walkthrough. |
Contact
For technical questions about this challenge, reach out to your contact at Brainforge. Do not open public GitHub issues about the challenge.
This assessment is used by Brainforge for Data Engineering candidates (Stage 3). Content owner: Awaish.