Data Quality & Observability
Knowing whether data is correct, complete and on time.
Backend Engineer track
Junior
Write correct code, ship small changes safely, ask good questions.
Nothing here yet.
Mid-level
Own a feature end to end without hand-holding.
Nothing here yet.
Senior
Own a system, its failure modes, and its trade-offs.
- Data QualityMaking sure data is accurate, complete and fresh.
Staff
Shape how many teams build, across systems.
Nothing here yet.
Principal
Set technical direction for the organization.
Nothing here yet.
Data Analyst track
Junior
Write correct SQL, build trusted dashboards, ask good questions.
Nothing here yet.
Mid-level
Own an analysis end to end, from vague question to recommendation.
Core: start here
- Data Quality DimensionsAccuracy, completeness, consistency, timeliness, validity and uniqueness.
5 more mid-level concepts
- Anomaly Detection on DataFlagging unusual volumes or values automatically.
- Data DowntimePeriods when data is missing, late or wrong.
- Data Expectations / AssertionsDeclared rules data must satisfy, checked in the pipeline.
- Data FreshnessHow recently a table was updated, and whether that's recent enough.
- Data TestsAutomated checks like not-null, unique and accepted values on tables.
Senior
Own experimentation and metrics design; call out bad numbers.
- Data ObservabilityMonitoring freshness, volume, schema and distributions to catch silent breakage.
- Data ReconciliationChecking that totals match between source and destination.
- Data SLAs and SLOsPromises about when data will be ready and how correct it will be.
Staff
Shape how the organization measures and decides.
Nothing here yet.
Principal
Set measurement strategy across the company.
Nothing here yet.
Data Engineer track
Junior
Build and fix pipelines from clear specs; write correct SQL.
- Data FreshnessHow recently a table was updated, and whether that's recent enough.
- Data QualityMaking sure data is accurate, complete and fresh.
- Data Quality DimensionsAccuracy, completeness, consistency, timeliness, validity and uniqueness.
- Data TestsAutomated checks like not-null, unique and accepted values on tables.
Mid-level
Own pipelines and models end to end, including their quality.
Core: start here
- Data IncidentA data quality failure that reaches users, and how to respond.
- Data ObservabilityMonitoring freshness, volume, schema and distributions to catch silent breakage.
- Data ReconciliationChecking that totals match between source and destination.
3 more mid-level concepts
- Anomaly Detection on DataFlagging unusual volumes or values automatically.
- Data DowntimePeriods when data is missing, late or wrong.
- Data Expectations / AssertionsDeclared rules data must satisfy, checked in the pipeline.
Senior
Design the platform's storage, processing and modeling choices.
Core: start here
- Data SLAs and SLOsPromises about when data will be ready and how correct it will be.
- Write-Audit-PublishWriting to a staging table, validating it, then swapping it into production.
1 more senior concepts
- Data Circuit BreakerStopping downstream jobs when upstream data fails checks.
Staff
Shape how the whole organization produces and uses data.
Nothing here yet.
Principal
Set data strategy and architecture across the company.
Nothing here yet.