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Data Quality Engineer Job Description Data Governance & Lineage: Proven experience enforcing data governance policies, managing data catalog, and mapping end-to-end Data Lineage from source to visualization layers.Strong SQL & Data Profiling: Advanced proficiency in writing complex SQL queries to profile datasets, monitor schema drift, and identify structural anomalies.Python Programming: Strong scripting skills in Python to build automated validation tests and interact with APIs/governance frameworks.Modern Data Stack (MDS): Hands-on architecture experience with cloud data platforms like Snowflake or Databricks.Data Quality Tools: Practical experience using testing and governance frameworks such as Great Expectations, dbt test, or enterprise catalog (e.g., Collibra, Informatica).Data Pipelines & Orchestration: Solid understanding of ETL/ELT pipelines and orchestration tools (Apache Airflow or Prefect) to embed quality checks and compliance controls directly into data workflows.Preferred / Nice-to-Have: Familiarity with data privacy regulations (e.g., GDPR, CCPA) and data masking techniques.Experience with Big Data processing (Apache Spark/PySpark).Experience with Data Observability platforms.Mandate: Collibra / Alation / Microsoft Purview Data Quality Engineer Job Description Data Governance & Lineage: Proven experience enforcing data governance policies, managing data catalog, and mapping end-to-end Data Lineage from source to visualization layers.Strong SQL & Data Profiling: Advanced proficiency in writing complex SQL queries to profile datasets, monitor schema drift, and identify structural anomalies.Python Programming: Strong scripting skills in Python to build automated validation tests and interact with APIs/governance frameworks.Modern Data Stack (MDS): Hands-on architecture experience with cloud data platforms like Snowflake or Databricks.Data Quality Tools: Practical experience using testing and governance frameworks such as Great Expectations, dbt test, or enterprise catalog (e.g., Collibra, Informatica).Data Pipelines & Orchestration: Solid understanding of ETL/ELT pipelines and orchestration tools (Apache Airflow or Prefect) to embed quality checks and compliance controls directly into data workflows.Preferred / Nice-to-Have: Familiarity with data privacy regulations (e.g., GDPR, CCPA) and data masking techniques.Experience with Big Data processing (Apache Spark/PySpark).Experience with Data Observability platforms.Mandate: Collibra / Alation / Microsoft Purview
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