Job Title:
Senior Data Quality Engineer
Experience:
6–8 Years
Job Description:
• Own end-to-end
data quality validation
across ingestion, transformation, and consumption layers
• Design and maintain
ETL automation frameworks
using Python and Pytest
• Perform
source-to-target validation, reconciliation, and anomaly detection
• Validate
cloud-based data architectures
(data lakes, warehouses, streaming platforms)
• Implement
data quality rules
such as null checks, schema validation, and integrity checks
• Use
DataGaps (preferred)
or similar tools for data quality monitoring
• Collaborate with
data engineers, architects, and product teams
to ensure quality
• Validate
ETL workflows, scheduling, and failure handling mechanisms
• Execute
SQL and Python-based validations
for batch and real-time data
• Integrate
automated data tests into CI/CD pipelines
• Track and report defects using
Zephyr and Jira
• Ensure compliance with
data governance and regulatory standards
• Mentor junior engineers and contribute to
best practices and frameworks
Required Skills:
• Strong experience in
data pipeline validation, ETL testing, and data quality engineering
• Proficiency in
Python, PySpark, SQL, and Pytest
• Experience with
Selenium and DataGaps frameworks
• Knowledge of
CI/CD tools (GitHub Actions) and version control (Git)
• Experience with
AWS (Glue, Iceberg) and lakehouse architectures
• Familiarity with
test data management techniques
• Experience with
performance testing tools (JMeter)
• Experience with
Zephyr test management tool
• Knowledge of
Docker and Kubernetes
Preferred:
• Experience with
healthcare or regulated datasets
• Experience validating
APIs, Tableau reports, and UI
• Familiarity with
Agile methodologies
Education:
• Bachelor’s degree in
Computer Science, Engineering, or related field
