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Great Expectations Quality

⬢ TIER 2Tech
💰
Medium
Salary impact
⏱
4 months
Time to learn
📊
Medium
Difficulty
👥
1
Careers
At a glance

Great Expectations is an open-source Python library for testing and validating data quality. Data engineers write 'expectations' (e.g., 'age column must be between 0-150') that run automatically on data pipelines. Anomalies trigger alerts. Advanced practitioners build organization-wide data quality infrastructure, catching 70-80% of issues before they reach analytics or ML. Salaries: $95-155k (USA). Mastery takes 3-4 months because it's primarily Python + SQL + domain knowledge.

What is Great Expectations Quality

Great Expectations is a Python library for data quality validation. Data engineers define 'expectations' (rules about data, nulls, ranges, uniqueness, regex patterns) and run them on data pipelines. When expectations fail, the system alerts engineers before bad data reaches analytics or ML models. Advanced practitioners build organization-wide data quality infrastructure: profiling datasets to auto-generate expectations, tracking expectation pass/fail rates, and integrating with orchestration tools (Airflow, Prefect) and notification systems (Slack, PagerDuty).

🔧 TOOLS & ECOSYSTEM
Great Expectations libraryPython pandasAirflow or PrefectPandas profilerSQL validatorsJupyter notebooksData docs generatorSlack integrationsCustom validatorsPostgreSQL or Snowflake

📋 Before you start

Python

💰 Salary by region

RegionJuniorMidSenior
🇺🇸USA$75k$115k$165k
🇬🇧UK$45k$70k$100k
🇪🇺EU$50k$78k$110k
🇨🇦CANADA$80k$125k$180k

🎓 Certifications

Great Expectations Official Documentation→DataCamp, Data Quality & Testing→Udemy, Data Quality with Great Expectations→

🎯 Careers using Great Expectations Quality

Ml Platform Engineer

❓ FAQ

▶What's the difference between a column expectation and a dataset expectation?
Column expectation: 'all values in age column are integers between 0-150.' Dataset expectation: 'row count doesn't exceed previous day by 50%.' Column expectations catch bad values; dataset expectations catch anomalous volume changes or data loss.
▶How do I integrate Great Expectations into an Airflow pipeline?
Add a GX task after data ingestion. Task runs expectations on loaded data. If expectations fail, task fails, Airflow stops and alerts (Slack, PagerDuty). Use CheckpointConfig to define which expectations run per dataset.
▶Can Great Expectations catch data drift?
Yes, by setting dynamic thresholds. 'Mean of metric yesterday ± 10%' becomes today's expectation. If today's data exceeds bounds, expectation fails. Requires tracking historical statistics (store them in database or config).
▶How many expectations should I write?
Start with 5-10 critical expectations per dataset (nullability, uniqueness, value range). Add more as you identify patterns. Target: 80% of common issues caught by first 20% of expectations (Pareto principle). Don't write hundreds; focus on high-impact ones.
▶What do I do when an expectation fails?
Log it (Slack, Datadog, custom webhook). Investigate root cause (upstream data issue, pipeline bug, legitimate data change). Fix root cause or adjust expectation if it's a valid new pattern. Update checkpoint to stay aligned with reality.
🎯

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