Skill Guide

Data quality validation using Great Expectations and custom assertion frameworks

The systematic process of programmatically testing datasets against predefined rules and business logic to ensure accuracy, completeness, and consistency, using the Great Expectations (GX) framework and custom code.

It prevents flawed data from corrupting analytics and machine learning models, directly reducing costly business errors and decision-making risks. By automating validation, it accelerates data pipeline development and builds institutional trust in data products.

1 Careers

1 Categories

8.7 Avg Demand

20% Avg AI Risk

How to Learn Data quality validation using Great Expectations and custom assertion frameworks

1. Understand core data quality dimensions (completeness, validity, timeliness). 2. Learn Python and basic SQL; install and configure Great Expectations (GX) in a local environment. 3. Write and run simple Expectations (e.g., expect_column_values_to_not_be_null) on sample CSV files.

1. Implement validation within ETL workflows using GX's Checkpoints and Data Docs. 2. Integrate GX with cloud data platforms (Snowflake, BigQuery) and orchestration tools (Airflow). 3. Avoid common pitfalls: over-reliance on built-in expectations without business context, and failing to version control expectation suites.

1. Architect organization-wide data quality frameworks that combine GX with custom assertion code for complex business rules. 2. Design monitoring, alerting, and self-healing systems for pipeline failures. 3. Mentor teams on data quality as a product, aligning validation SLAs with business KPIs.

Practice Projects

Beginner

Project

Validate a Public Dataset with Great Expectations

Scenario

You have a CSV file of NYC taxi trip records. The goal is to ensure key columns like `fare_amount` and `passenger_count` are valid before any analysis.

How to Execute

1. Download the dataset and initialize a GX Data Context. 2. Create a Datasource and Data Asset pointing to the CSV. 3. Build an Expectation Suite interactively using a Jupyter notebook, adding expectations like `expect_column_values_to_be_between` for `fare_amount`. 4. Run validation and review the HTML Data Docs report.

Intermediate

Project

Integrate GX into a dbt Cloud Pipeline

Scenario

A dbt model that creates a `fct_orders` table needs pre- and post-load validation to catch anomalies before they reach the BI dashboard.

How to Execute

1. Use the `dbt-gx` integration to generate a Data Asset from a dbt model's SQL. 2. Define an Expectation Suite (e.g., `expect_table_row_count_to_be_between`, `expect_column_values_to_be_unique` for `order_id`). 3. Create a GX Checkpoint and run it as a dbt test or a separate CI/CD step. 4. Configure Slack alerts for validation failures.

Advanced

Project

Build a Custom Assertion Framework for Regulatory Data

Scenario

Financial transaction data must adhere to complex, jurisdiction-specific rules (e.g., cross-border transfer limits) that are not covered by standard expectations.

How to Execute

1. Design a Python module with custom Assertion classes that inherit from `great_expectations.expectations.expectation.Expectation`. 2. Implement the `get_validation_dependencies` and `_validate` methods to encode complex business logic and external API checks. 3. Package assertions into a reusable GX plugin and deploy it to your cloud environment. 4. Create a governed, versioned Expectation Suite that blends GX core and custom assertions, integrated into a mandatory pipeline gate.

Tools & Frameworks

Core Software & Platforms

Great Expectations (GX)Apache Airflow / Prefectdbt

GX is the central validation engine. Airflow/Prefect orchestrate validation checkpoints as pipeline tasks. dbt integrates for model-level data quality testing.

Data Platform & Cloud Integrations

SnowflakeGoogle BigQueryAWS Glue

GX connects directly to these platforms via SQLAlchemy or native APIs, allowing validation to run in-place on production data without data movement.

Supporting Libraries

PandasPydanticSQLAlchemy

Used for custom data manipulation, defining strict data models for assertions, and providing database connectivity to GX Datasources.

Interview Questions

Answer Strategy

Structure the answer around: 1) Defining quality dimensions based on model needs (e.g., label stability, feature drift). 2) Choosing Great Expectations for its extensibility and documentation. 3) Explaining where to place validations (pre-ingestion, post-transformation, pre-model serving). 4) Discussing custom assertions for business-specific logic and how to handle failures (quarantine, alert, auto-correct).

Answer Strategy

Tests problem-solving, ownership, and systems thinking. Use the STAR method. Focus on the 'systemic fix'-moving from ad-hoc checks to automated, monitored validation.