Overview
What this challenge is about.
Merge AWS, GCP, and Lambda Labs billing into Parquet, tag spend, and build a cost dashboard. Get a verifiable certificate.
The scenario
The consultancy (around 35 staff, around EUR 8M revenue) burned through around EUR 60k of unattributed GPU spend last quarter; the partner team wants a tool, not another spreadsheet.
The Brief
What you'll do, and what you'll demonstrate.
Ship a per-client GPU cost dashboard that flags unattributed spend and lets partners catch unprofitable engagements within a week.
Earning criteria — what you'll demonstrate
- Reconcile billing data across multiple cloud providers
- Design a cost-attribution model that handles tagging gaps
- Build a partner-facing dashboard with action-oriented views
- Document a tagging convention engineering will actually adopt
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Cloud Computing for Data and ML
Master · Data Engineering
Strong alignment
This challenge maps to Cloud Computing for Data and ML at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Cloud Cost Attribution
Apply cloud cost attribution to solve real industry problems and demonstrate production-level capability.
- Etl Pipelines
Apply etl pipelines to solve real industry problems and demonstrate production-level capability.
- Data Modeling
Apply data modeling to solve real industry problems and demonstrate production-level capability.
- Dashboarding
Apply dashboarding to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Documentation
Apply documentation to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Engineer
Multi-cloud billing reconciliation + dashboard is the kind of pragmatic data-engineering project that ships value in week one of a new job.
This challenge sharpens
- etl-pipelines
- data-modeling
- cloud-cost-attribution
MLOps Engineer
Cost attribution for GPU experiments is MLOps-adjacent work that keeps research budgets honest at any AI company.
This challenge sharpens
- cloud-cost-attribution
- etl-pipelines
- dashboarding
AI Product Manager
Designing partner-facing cost views with clear action triggers is the AI PM's craft of turning data into decisions.
This challenge sharpens
- dashboarding
- documentation
- data-modeling