Data Quality Engineer QA
RemoteHungary
Job Summary
Design and implement a comprehensive data quality framework covering completeness, accuracy, consistency, timeliness, and uniqueness across the enterprise lakehouse platform. Build automated test suites using Great Expectations, dbt, or Soda Core, and develop dashboards to provide visibility to producers and consumers. Integrate quality checks into CI/CD pipelines to prevent bad data from reaching production while defining and managing SLAs at the pipeline and data product level. Triage and resolve data quality incidents by coordinating with upstream source owners and profile new data sources during onboarding to establish baseline benchmarks. Collaborate with the data governance team to enforce quality rules aligned with business glossary definitions and educate engineering teams on measurement approaches. This role supports Dynata's mission to deliver precise, trustworthy first-party data insights to over 6,000 global agencies and customers.
Required Qualifications
- 4+ years in data engineering, data quality, or analytics engineering roles
- Hands-on experience with data quality tools: Great Expectations, dbt tests, Soda, Monte Carlo, or similar
- Strong SQL and Python skills for writing validation logic and quality metric queries
- Experience with Apache Spark or dbt for large-scale data transformation and testing
- Familiarity with data profiling techniques and statistical approaches to anomaly detection
- Knowledge of data observability concepts and their application in lakehouse environments
- Experience with Airflow or similar orchestration tools to schedule quality jobs
- Bachelor's degree in Computer Science, Statistics, Engineering, or related field
- Currently based in Hungary
- Have the legal right to live and work in Hungary
Desired Qualifications
- Experience with Databricks or Snowflake data quality native features
- Knowledge of DataHub or data catalog integration for quality score publishing
- Familiarity with data contracts and shift-left quality approaches in data mesh architectures
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