Audit Data Quality for a Climate Tech Sensor Network
Overview
What this challenge is about.
Audit 400M climate sensor rows, detect stuck-sensor and drift anomalies, build a quality scorecard. Earn a verifiable certificate.
The scenario
The startup (around 80 staff, around EUR 9 million annual revenue) sells to municipal procurement departments where data-quality SLAs are increasingly a checkbox item in tenders.
The Brief
What you'll do, and what you'll demonstrate.
Build a data-quality layer that surfaces silent sensor failures and produces a defensible quality report for customer-facing SLAs.
Earning criteria — what you'll demonstrate
- Express data-quality intent declaratively (expectations, contracts)
- Implement basic anomaly detection appropriate to time-series sensor data
- Aggregate quality signals into a customer-facing scorecard
- Translate engineering quality metrics into a sales-grade SLA
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Data Engineering and Big Data Systems
Master · Data Engineering
Strong alignment
This challenge maps to Data Engineering and Big Data Systems 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.
- Data Quality
Apply data quality to solve real industry problems and demonstrate production-level capability.
- Great Expectations
Apply great expectations to solve real industry problems and demonstrate production-level capability.
- Anomaly Detection
Apply anomaly detection to solve real industry problems and demonstrate production-level capability.
- Sql
Query, transform, and analyze relational data to extract meaningful business insights.
- Monitoring
Apply monitoring to solve real industry problems and demonstrate production-level capability.
- Dashboarding
Apply dashboarding 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
Data-quality monitoring is a top-five responsibility on most senior data-engineer job descriptions; this challenge proves a student can scope and ship one.
This challenge sharpens
- data-quality
- great-expectations
- monitoring
Data Scientist
Anomaly-detection design on real-world time-series with messy edges is daily work for data scientists on operations or trust-and-safety teams.
This challenge sharpens
- anomaly-detection
- sql
- dashboarding
MLOps Engineer
Production data-quality monitoring is increasingly owned by MLOps; the alerting and SLA framing transfers directly.
This challenge sharpens
- monitoring
- data-quality
- anomaly-detection