01
Quality checks
Make this concrete enough that another engineer can understand what you built and why.
CAREER PROJECT · Advanced · 2 weekends
Data Quality Monitoring Pipeline
Detect freshness, schema and distribution problems before broken data reaches dashboards.
Data qualityPythonSQLMonitoringPipelines
DEFINITION OF DONE
Leave artifacts a reviewer can inspect.
02
Alert demo
Make this concrete enough that another engineer can understand what you built and why.
03
Incident examples
Make this concrete enough that another engineer can understand what you built and why.
04
Runbook
Make this concrete enough that another engineer can understand what you built and why.
05
Dashboard
Make this concrete enough that another engineer can understand what you built and why.
RESUME / PORTFOLIO FRAMING
Describe what you actually did.
Implemented automated data-quality monitoring for freshness, schema and distribution anomalies with actionable alerts.
Use this as a starting structure, then replace it with your real implementation details, metrics and constraints. Do not claim outcomes you did not measure.
STRETCH GOALS
Go deeper if the core is already solid.
Lineage-aware alerts
Add only if it strengthens the engineering story instead of delaying a finished, deployable core.
Anomaly models
Add only if it strengthens the engineering story instead of delaying a finished, deployable core.
SLA tracking
Add only if it strengthens the engineering story instead of delaying a finished, deployable core.