Audit Data Quality for a Climate Tech Sensor Network
Overview
What this challenge is about.
You receive 30 days of ingested sensor data (around 400 million rows) plus the sensor inventory and known maintenance windows. Define a set of data-quality expectations (null rate, value range, freshness, distribution drift) using Great Expectations or Soda. Implement anomaly detection for stuck-sensor and drift patterns. Generate a per-sensor quality scorecard and a city-level quality dashboard. The deliverable is the monitoring code, the 30-day quality report, and a short proposal for SLA-grade quality commitments the sales team can use with municipalities.
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.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
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