Skill Guide

Data quality frameworks including Great Expectations, dbt tests, and custom validators

Data quality frameworks are systematic approaches and toolkits used to define, measure, and enforce expectations about data validity, completeness, and consistency throughout the data pipeline.

This skill is critical for ensuring data reliability, which directly supports accurate analytics, machine learning model performance, and trustworthy business intelligence. It prevents costly downstream errors and maintains stakeholder confidence in data-driven decision-making.

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How to Learn Data quality frameworks including Great Expectations, dbt tests, and custom validators

Focus on understanding core data quality dimensions (completeness, accuracy, consistency, timeliness), learning the YAML/JSON configuration syntax for tools like Great Expectations, and mastering basic dbt test macros (unique, not_null, accepted_values).

Move to integrating Great Expectations into a dbt pipeline, building custom expectation suites for business-specific rules, and implementing CI/CD checks for data contracts. Avoid over-testing low-risk assets and learn to interpret data docs for root-cause analysis.

Master the architecture of enterprise data quality systems, including orchestration with Airflow/Prefect, metric history tracking, and designing organizational data quality SLAs. Focus on mentoring teams on framework adoption and aligning quality metrics with business KPIs.

Practice Projects

Beginner

Project

Set Up a Basic Data Quality Check Suite

Scenario

You have a CSV file of customer orders and need to validate its structure and key fields before loading it into a database.

How to Execute

1. Install Great Expectations and initialize a project. 2. Connect to the CSV file as a Datasource. 3. Create an Expectation Suite with basic checks (column existence, data type, value ranges). 4. Run a Checkpoint to validate the data and generate a Data Doc report.

Intermediate

Project

Integrate dbt Tests with a Custom Great Expectations Validator

Scenario

Your dbt model for 'fact_sales' requires a business rule check that is too complex for standard dbt tests, such as ensuring 'discount_percentage' is within a valid range based on 'product_category'.

How to Execute

1. Write a custom Expectation in Great Expectations using Python to encode the complex business logic. 2. Create a dbt model and add a test that calls the GE validation via a pre-hook or post-hook command. 3. Use the 'ge_checkpoint' CLI command within dbt to run validation and fail the pipeline on error. 4. Review the integrated report in your dbt docs or GE Data Docs.

Advanced

Project

Design an Enterprise Data Quality Monitoring Platform

Scenario

You are leading the data platform team and need to implement proactive, cross-pipeline quality monitoring with alerting and SLA tracking for critical datasets.

How to Execute

1. Architect a solution using Great Expectations with a cloud-based metadata store (e.g., AWS S3) for expectation history. 2. Integrate validation checkpoints into your orchestrator (Airflow) for every critical pipeline run. 3. Build a dashboard (e.g., in Looker or Grafana) that surfaces quality metrics and SLA adherence. 4. Implement automated alerting via Slack/PagerDuty for failed validations and establish a remediation runbook.

Tools & Frameworks

Software & Platforms

Great Expectationsdbt (dbt tests)Soda CorePydantic (for custom validators)

Great Expectations is the industry standard for declarative data validation with rich documentation. dbt's built-in testing is essential for analytics engineering pipelines. Soda Core offers a simpler SQL-based alternative, and Pydantic is invaluable for building custom, schema-driven validators in Python applications.

Conceptual Frameworks

Data ContractsData Mesh Quality PillarsObservability (OpenTelemetry for data)

Data Contracts formalize quality expectations between producers and consumers. Applying Data Mesh principles treats quality as a federated, product-centric responsibility. Observability frameworks extend monitoring beyond pass/fail to understanding the state and drift of data systems.

Interview Questions

Answer Strategy

The candidate must demonstrate an understanding of stakeholder alignment and tiered quality strategies. They should outline a plan using dbt for modeling and unit tests, Great Expectations for pipeline integration, and custom validators for ML-specific checks (e.g., feature drift). Sample answer: 'I would establish a data contract with both teams. For BI, I'd implement strict dbt tests for dimension integrity and freshness. For the feature store, I'd add custom GE expectations to validate statistical properties (mean, variance) and feature distribution against a baseline, running these checks in the feature pipeline's pre-flight.'

Answer Strategy

This tests root-cause analysis and preventive thinking. The answer should follow the STAR method, focusing on technical depth and systematic improvement. Sample answer: 'A daily revenue report was misstated due to a null in a currency conversion field. I used GE's Data Docs to trace the failure to a source API change. The fix was adding a not_null and regex test to that column. To prevent recurrence, I implemented a Data Contract with the API owner and added the expectation to our CI/CD suite, so similar schema changes now break the build.'