Lead Data QA Automation Architect

Cognizant; Australia; On-site; AUD 100,000 - 130,000; Full time

Job summary

Cognizant is seeking a Lead Data QA Automation Engineer / QA Architect to lead data quality assurance, ETL testing and automated validation across large cloud migration programs. The role demands expertise in data validation, governance, and enterprise data platforms, with hands-on work in SQL, Python, PySpark and DBT.

You will design reusable automation frameworks for batch and real-time ingestion spanning APIs, SAP, legacy systems, and cloud-native ecosystems like Azure and Databricks.

Qualifications

• Advanced ETL testing, data validation and migration assurance in Australian Insurance domains.
• Design and build scalable data QA automation frameworks with DBT.
• Lead stakeholder management and global delivery coordination.

Responsibilities

• Lead end-to-end data testing and validation for multiple domains.
• Define and implement scalable ETL and data QA automation frameworks.
• Validate migrations from legacy platforms to cloud environments.
• Perform source-to-target validation across core datasets.
• Ensure data quality in batch and real-time pipelines (APIs/SAP).
• Establish governance, validation approaches and compliance controls.
• Collaborate with data engineers, architects and delivery teams.
• Conduct profiling, reconciliation and root cause analysis.
• Provide delivery reporting and quality metrics to leadership.

Skills

Advanced SQL

Python

PySpark

Data QA

Test Automation

Stakeholder management

Agile/Scrum

Education

Bachelor of Engineering in Electronics and Telecommunications

CDAC Certification

Tools

DBT

GitHub Co-pilot

Azure Data Factory

Azure Synapse

Databricks

Delta Lake

Informatica PowerCenter

Informatica IICS

Talend

SAP BODS

Duck Creek

Job description

Cognizant is seeking a Lead Data QA Automation Engineer / QA Architect to lead data quality assurance, ETL testing and automated validation across large cloud migration programs. The role demands expertise in data validation, governance, and enterprise data platforms, with hands-on work in SQL, Python, PySpark and DBT.

You will design reusable automation frameworks for batch and real-time ingestion spanning APIs, SAP, legacy systems, and cloud-native ecosystems like Azure and Databricks.