Data quality engineering using Great Expectations, Deequ, or Soda
The practice of implementing automated, programmatic data validation, monitoring, and observability pipelines using specialized frameworks to ensure data assets are complete, accurate, consistent, and timely.
It directly protects revenue and operational integrity by preventing 'garbage-in-garbage-out' scenarios in analytics and ML models, which can cost enterprises millions in faulty decisions. This skill shifts data quality from a reactive, manual bottleneck to a proactive, engineering-led function, accelerating time-to-insight and enabling trust in data-driven initiatives.
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9.1 Avg Demand
15%
Avg AI Risk
How to Learn Data quality engineering using Great Expectations, Deequ, or Soda
1. **Core Concepts:** Understand the 6 dimensions of data quality (completeness, accuracy, consistency, timeliness, uniqueness, validity). 2. **Tool Mechanics:** Install and run basic data validation on a CSV file using one framework (e.g., Great Expectations `expect_column_values_to_not_be_null`). 3. **YAML/JSON Proficiency:** Learn to define and version control Expectation Suites or test definitions as code.
1. **Integration:** Connect a framework to a production database (e.g., PostgreSQL) and a data warehouse (e.g., Snowflake, BigQuery). 2. **Pipeline Integration:** Implement data quality checks as a stage in an existing orchestration tool (Airflow, Dagster, Prefect). 3. **Common Pitfall Avoidance:** Do not create static, overly broad Expectations. Design checks based on business logic and data profiling, not just schema.
1. **Architectural Strategy:** Design a centralized data quality service that serves multiple teams with shared Expectation libraries and SLAs. 2. **Root Cause Analysis:** Implement advanced anomaly detection and statistical profiling to move beyond rule-based checks. 3. **Mentorship & Governance:** Establish data quality SLAs, define escalation paths for quality failures, and coach data teams on writing effective tests.
Practice Projects
Beginner
Project
Validate a Public Dataset with Great Expectations
Scenario
You have a messy CSV of New York City taxi trip data (missing values, incorrect data types, outliers).
How to Execute
1. `pip install great_expectations` and initialize a GX project (`gx init`). 2. Connect to the CSV as a Datasource and create an Expectation Suite. 3. Use the Data Docs UI or a notebook to generate expectations like `expect_column_values_to_be_between` for trip distance and `expect_column_values_to_not_be_null` for fare. 4. Run a Checkpoint to validate the data and review the HTML Data Docs report.
Intermediate
Project
Integrate Soda Core Checks into an Airflow DAG
Scenario
Your daily Airflow DAG loads product inventory data into a Snowflake data warehouse. You need to halt downstream transformations if data freshness or critical metric thresholds are breached.
How to Execute
1. Define Soda checks in a YAML file (`checks.yaml`) with SQL-based tests (e.g., `row_count > 0`, `freshness: { column: 'updated_at', max_age: '24h' }`). 2. Use the `soda-core-snowflake` package in your Airflow environment. 3. Create a `PythonOperator` or `BashOperator` task that executes `soda scan -d my_snowflake -c configuration.yml -s checks.yml`. 4. Use XCom to pass the exit code and fail the DAG if the scan returns a non-zero code.
Advanced
Project
Establish a Cross-Team Data Quality Service with Deequ
Scenario
Multiple data engineering teams in your organization are building ETL pipelines on AWS EMR/Spark. Quality is ad-hoc and inconsistent.
How to Execute
1. Create a shared Deequ Jupyter notebook library with custom `Check` objects and `VerificationSuite` configurations tailored to common business entities (e.g., `Customer`, `Transaction`). 2. Build an automated API or CLI tool that runs these Deequ checks on any S3-based Spark DataFrame and publishes metrics to CloudWatch. 3. Implement a central dashboard (e.g., in Grafana) tracking key quality metrics over time. 4. Define and enforce a policy where pipeline CI/CD must include a Deequ verification step with a minimum quality score to merge.
Tools & Frameworks
Core Validation Frameworks
Great Expectations (GX)AWS Deequ (for Spark)Soda Core / Soda Cloud
GX is the Python-centric standard for unit tests for data, excellent for complex pipelines and rich documentation. Deequ is a Spark-native library for large-scale, statistical data profiling and validation. Soda Core offers a simple YAML/SQL syntax ideal for quick integration and business-user accessibility.
Orchestration & Monitoring
Apache AirflowDagsterMonte Carlo / Bigeye (Data Observability Platforms)
Airflow and Dagster are used to trigger and manage validation jobs as pipeline steps. Dedicated observability platforms provide anomaly detection, lineage, and root-cause analysis that complement rule-based testing.
Cloud Data Platforms
SnowflakeDatabricks (Delta Lake)Google BigQuery
Understanding how to run validation queries directly within the data warehouse (e.g., using Snowflake tasks with GX) is critical for performance and avoiding data movement.
Interview Questions
Answer Strategy
The candidate must demonstrate **stage-based validation thinking** (in-pipeline vs. warehouse) and **metric-specific checks**. A strong answer covers: 1) Validating raw stream data (schema, nulls), 2) Applying business logic checks post-aggregation (e.g., DUA > 0, DUA vs. historical average ± 3σ), 3) Implementing freshness checks, and 4) Alerting and dashboard annotation on failure.
Answer Strategy
The core competency tested is **strategic prioritization and incremental rollout**. The answer must show a methodical, non-disruptive approach. Focus on: 1) **Profile First:** Understand the data before writing rules. 2) **Target Critical Paths:** Prioritize checks on upstream, high-impact tables. 3) **Implement in 'Warn' Mode:** Start with non-blocking checks. 4) **Collaborate:** Use the data scientists' complaints to define specific, impactful checks.
Careers That Require Data quality engineering using Great Expectations, Deequ, or Soda