Data quality engineer

peopleimpact

Required skills

Responsibilities include:

Validate ETL logic, business logic, and data quality in Snowflake, Databricks, and other data platforms before code changes are released to production.
Partner with data engineers to identify potential failure points and proactively help catch issues early.
Ensure the quality of every release using rigorous, data-driven testing practices.
Develop automated and reusable tests to improve coverage, development velocity, and reduce regression risk.
Translate business and technical requirements into test scenarios to validate KPIs, metrics, and business rules.
Contribute to and enhance the existing test automation framework, with a focus on scalability and maintainability.
Collaborate closely with Data Analysts, Product Managers, and Engineering teams to ensure accuracy, completeness, and usability of the data.

Required Education, Experience/Skills/Training:

Strong experience validating data pipelines, ETL processes, and data warehouses in production environments.
Expert-level SQL skills and hands-on experience working with large datasets (terabytes or more), capable of identifying data anomalies through efficient queries.
Proficiency with Snowflake, Hive, Databricks, and other modern data platforms.
Solid Python skills and experience with test automation for data pipelines.
Familiarity with tools like Airflow and Spark and understanding of CI/CD principles.
Strong collaboration and communication skills; able to work effectively across cross-functional teams.
B.S. in Computer Science (or equivalent degree or work experience)

Nice to have:

Experience with BDD frameworks (e.g., Behave)
Experience working in AWS or other cloud environments
Familiarity with open-source data quality tools like Deequ, Great Expectations, or similar custom frameworks

MNC

This page is fully interactive when JavaScript is enabled. Please enable JavaScript to apply or browse related roles.