Data Quality Engineer

1. Data Governance & Lineage: Proven experience enforcing data governance policies, managing data catalog, and mapping end-to-end Data Lineage from source to visualization layers.
2. Strong SQL & Data Profiling: Advanced proficiency in writing complex SQL queries to profile datasets, monitor schema drift, and identify structural anomalies.
3. Python Programming: Strong scripting skills in Python to build automated validation tests and interact with APIs/governance frameworks.
4. Modern Data Stack (MDS): Hands-on architecture experience with cloud data platforms like Snowflake or Databricks.
5. Data Quality Tools: Practical experience using testing and governance frameworks such as Great Expectations, dbt test, or enterprise catalog (e.g., Collibra, Informatica).
6. 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:

1. Familiarity with data privacy regulations (e.g., GDPR, CCPA) and data masking techniques.
2. Experience with Big Data processing (Apache Spark/PySpark).
3. Experience with Data Observability platforms.