Data quality frameworks and validation (Great Expectations, Pydantic, dbt tests)
Data quality frameworks and validation involve systematically applying software tools and methodologies to define, test, and monitor data integrity, accuracy, and consistency throughout its lifecycle.
This skill is critical because it directly reduces operational risk, prevents costly data-driven decision errors, and automates the maintenance of data contracts between producers and consumers. High data reliability is a prerequisite for trustworthy analytics, machine learning models, and regulatory compliance, directly impacting operational efficiency and strategic confidence.
1Careers
1Categories
8.7 Avg Demand
25%
Avg AI Risk
How to Learn Data quality frameworks and validation (Great Expectations, Pydantic, dbt tests)
Focus on three core areas: 1) Understand the fundamental pillars of data quality (completeness, accuracy, timeliness, validity). 2) Master the basic syntax and execution of one primary tool, starting with dbt tests for SQL-centric validation. 3) Implement simple, column-level null-checks and range validations in a personal project to build the habit of 'test-as-you-build'.
Move from ad-hoc tests to systematic validation suites. Learn to use Great Expectations for profiling datasets and creating expectation suites, and use Pydantic for validating data schemas and API payloads in Python codebases. Common mistakes include testing too granularly without business context and creating brittle tests that break on benign schema evolution. Focus on integrating these tests into CI/CD pipelines.
Master the architecture of data quality at scale. This includes designing and implementing a centralized data observability platform using tools like Great Expectations Cloud or dbt Cloud's Advanced CI, establishing organizational data quality SLAs, and orchestrating complex validation workflows with tools like Prefect or Airflow. At this level, you mentor teams on building a 'quality-first' data culture and aligning validation metrics with business KPIs.
Practice Projects
Beginner
Project
Customer Data Validation with dbt
Scenario
You have a raw CSV of customer data loaded into a data warehouse. You need to ensure key fields like 'email' are valid and 'signup_date' is in the past.
How to Execute
1. Create a dbt model to stage the raw customer data. 2. Add basic dbt tests to the model's schema.yml file: test `not_null` on 'customer_id' and 'email', test `unique` on 'customer_id'. 3. Add a dbt test macro to validate that 'signup_date' is less than or equal to the current date. 4. Run `dbt test` and document the results.
Intermediate
Project
API Data Contract Validation with Pydantic
Scenario
A third-party API provides user activity data. You need to validate the JSON payload structure and data types before storing it in your database to prevent pipeline failures.
How to Execute
1. Define a Pydantic `BaseModel` class that mirrors the expected API response structure, specifying required fields, data types (e.g., `datetime`), and using validators for complex logic. 2. Write a Python function that parses the API response through this model. 3. Wrap the parsing in a try/except block to catch `ValidationError`, log the failure details, and route bad records to a quarantine table. 4. Integrate this function into your data ingestion script.
Advanced
Project
End-to-End Data Quality Pipeline with Great Expectations
Scenario
You own a critical analytics dashboard. Data flows from multiple source systems into a data lake, then into the warehouse for reporting. You need to enforce quality checks at every stage and alert on failures.
How to Execute
1. Use Great Expectations' CLI to connect to your data sources (lake, warehouse) and auto-generate an initial expectation suite via profiling. 2. Refine and add critical business-rule expectations (e.g., 'order_total' > 0, 'transaction_status' in allowed list). 3. Create a Checkpoint to run these suites against your daily data batches. 4. Integrate the Checkpoint into your Airflow/Prefect DAG, configuring it to send Slack alerts on failure and block downstream reporting tasks until issues are resolved.
Tools & Frameworks
Validation & Observability Tools
Great Expectationsdbt Tests & dbt-expectations packageSoda CorePandera
Great Expectations is an open-source Python library for data profiling, defining 'expectations' (tests), and generating documentation. Use it for comprehensive, multi-source validation. dbt Tests are built-in SQL-based assertions. The `dbt-expectations` package ports many Great Expectations tests to dbt. Soda Core uses a YAML-based SodaCL for simple, declarative checks. Pandera is for dataframe-centric validation in Python/pandas workflows.
Data Contract & Schema Tools
PydanticJSON SchemaAvro/Protobuf
Pydantic is used for runtime data validation and settings management in Python, ideal for validating API payloads and configuration files. JSON Schema defines the structure of JSON data. Avro and Protobuf are schema-evolution formats used for data serialization in streaming pipelines (Kafka) to enforce contracts between producers and consumers.
Orchestration & Integration
Apache AirflowPrefectDagsterCI/CD Systems (GitHub Actions, GitLab CI)
These tools are used to schedule, orchestrate, and monitor data validation workflows. Validation suites (from GE, dbt, Soda) are run as tasks within these pipelines. CI/CD systems are used to run validation tests on schema changes and pull requests before deploying to production.
Interview Questions
Answer Strategy
The interviewer is assessing architectural thinking and tool selection rationale. Use a layered approach. Sample answer: 'I'd use dbt for core, transformation-layer tests where business logic is expressed in SQL-things like referential integrity and uniqueness. For profiling raw source data and validating complex, cross-dataset statistical properties (e.g., distribution shifts), I'd integrate Great Expectations. The two would run in sequence: dbt tests during transformation, GE checkpoints on the final curated models, all orchestrated by Airflow.'
Answer Strategy
This behavioral question tests post-mortem rigor and systems thinking. Use the STAR method. Focus on the systemic fix, not just the band-aid. Sample answer: 'A dashboard showed zero revenue due to a NULL value introduced by a source API change. We fixed the immediate data. The root cause was our lack of a schema contract test for that API. I implemented a Pydantic validation layer for all API ingests, integrated it into our CI pipeline, and added a GE suite to check for NULL revenue post-aggregation. This created two defensive layers.'
Careers That Require Data quality frameworks and validation (Great Expectations, Pydantic, dbt tests)