Implement and maintain data quality frameworks, automated testing, and observability solutions to ensure trusted data across all platforms at APPIT Software in Amsterdam.
Amsterdam, Netherlands
Full-time
Data Engineering
Responsibilities
Design and implement data quality testing frameworks using Great Expectations, dbt tests, or Soda
Build automated data validation pipelines that run on every data load and transformation
Develop data quality scorecards and dashboards to track quality metrics across data domains
Investigate and perform root cause analysis on data quality incidents and anomalies
Collaborate with data engineers to embed quality checks into ETL/ELT pipeline stages
Define data quality SLAs, ownership models, and escalation procedures for critical data assets
Requirements
3-6 years of experience in data quality, data engineering, or data analytics
Hands-on experience with data quality tools (Great Expectations, Soda, dbt tests, or Monte Carlo)
Strong SQL skills for data profiling, anomaly detection, and validation query development
Proficiency in Python for building custom data quality checks and automation scripts
Understanding of data observability concepts including freshness, volume, schema, and distribution monitoring
Experience working with cloud data warehouses (Snowflake, BigQuery, or Redshift)
Nice to Have
Experience with data observability platforms like Monte Carlo or Elementary
Knowledge of statistical methods for anomaly detection
Familiarity with GDPR data quality requirements and compliance reporting
Skills
Great ExpectationsSQLPythondbtSnowflakeData ObservabilityData Profiling
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