Data Quality Engineer

Cyient is hiring for the role of Data Quality Engineer! Responsibilities of the Candidate: 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. Requirements: 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. Skills Required - Data Governance, SQL, Data Profiling, Python, Modern Data Stack, Data Quality Cyient is hiring for the role of Data Quality Engineer! Responsibilities of the Candidate: 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. Requirements: 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. Skills Required - Data Governance, SQL, Data Profiling, Python, Modern Data Stack, Data Quality