Job Description
Position Summary:
We are seeking a highly skilled Test Architect with Data Analyst skills who has expertise in Databricks, PySpark, MongoDB, and Azure Cloud to lead quality assurance team and data analytics initiatives across our enterprise data platforms. This role involves designing and implementing automated testing frameworks to validate data pipelines, transformations, and storage systems, ensuring high data quality and integrity. In addition to QA leadership, the role requires working with various data analytics tools and techniques to generate impactful insights that support strategic business decisions.
The ideal candidate will have a strong background in data warehouse technologies, cloud-based architecture and data modelling, with a solid understanding of business intelligence principles and cloud computing platforms such as Azure, AWS, and GCP. Familiarity with data architecture, data warehouse models, Kimball methodology, dimensional modelling, and the Data Vault approach is essential. The successful candidate will be ISTQB certified and have insurance domain and cloud certifications, bringing valuable domain knowledge in the insurance industry while collaborating with cross-functional teams to define testing standards, reduce quality costs, and ensure data integrity at scale.
Mandatory Skills:
Proven expertise in Data Warehouse Quality Assurance using cloud platforms such as Azure, AWS, Databricks, PySpark.
Strong data analysis capabilities, with the ability to interpret and validate complex datasets using a variety of data analysis tools to interpret and report on data
Experience in defining and implementing testing standards, methodologies, and best practices across cross-functional teams.
Hands-on proficiency in both structured and unstructured databases Spark-SQL, MongoDB, Unix, Oracle, Redshift, and PySpark.
Solid understanding of data modeling, SQL querying and data pipeline design.
Ability to comprehend and translate complex business requirements into effective testing strategies.
Practical experience working within an Agile development environment.
Familiarity with the Atlassian toolset, including JIRA, Confluence, Bitbucket, Jenkins, and Git.
In-depth insurance domain knowledge, with a focus on data-centric applications.
Strong understanding of data modeling techniques, including conceptual, logical, and physical data models
Experience in cloud data migration projects involving insurance COTS products such as Guidewire and Evolve
Experience in validating ML models, automating testing pipelines, and ensuring model robustness and fairness. Skilled in Python, PyTest, MLflow, and CI/CD tools using Jenkins and Docker.
Certified in ISTQB, Azure Cloud, and insurance domains
Duties and Responsibilities:
Prepare and own the end-to-end test strategy for Data Warehouse programs
Liaison with BSA to understand the requirements
Create / update the Requirements traceability matrix with test scenarios.
Create/Modify the SQL test scripts as per the ETL mapping documents and technical specifications & Requirements.
Run the jobs and perform smoke testing
Record and maintain testing evidence and track execution status of assigned test cases.
Analyze test results and defect data to identify trends and root causes.
Use data visualization tools (e.g., Power BI, Tableau, Python libraries) to present testing metrics and KPIs.
Develop predictive models to forecast defect density, test coverage gaps, and release readiness.
Perform exploratory data analysis (EDA) on production and test data to uncover quality issues
Identify defects and log failures.
Track defects (defect log as generated from the QC tool/JIRA Tool) to closure.
Participate in the defect triages to gather evidence for defect identification on periodic basis for defect prioritization and fix.
Internal review and share for stakeholder review and feedback. Collaborate closely with BA & development team
Ensure all test data aligns with privacy and compliance requirements across test environments
Lead the development of automated test suites for functional, performance, and security testing.
Design and implement automated testing pipelines for ML models using PyTest and MLflow.
Integrate test automation with CI/CD pipelines.
Monitor and maintain test environments and data integrity.
Qualifications & Certifications (Optional):
Bachelor of Engineering
Azure DP-900 certification (or any equivalent)
ISTQB certification
Insurance domain certification-LOMA 280 & 290 certified for insurance domain (or any equivalent).
Understanding of mining tools to automate turning unstructured text into structured data
Salary Range: >$100,000
Date of Posting: 31/July/2026
Next Steps: If you feel this opportunity suits you, or Cognizant is the type of organization you would like to join, we want to have a conversation with you! Please apply directly with us.
For a complete list of open opportunities with Cognizant, visit http://www.cognizant.com/careers. Cognizant is committed to providing Equal Employment Opportunities. Successful candidates will be required to undergo a background check.
#LI-CTSAPAC
We are seeking a highly skilled Test Architect with Data Analyst skills who has expertise in Databricks, PySpark, MongoDB, and Azure Cloud to lead quality assurance team and data analytics initiatives across our enterprise data platforms. This role involves designing and implementing automated testing frameworks to validate data pipelines, transformations, and storage systems, ensuring high data quality and integrity. In addition to QA leadership, the role requires working with various data analytics tools and techniques to generate impactful insights that support strategic business decisions.
The ideal candidate will have a strong background in data warehouse technologies, cloud-based architecture and data modelling, with a solid understanding of business intelligence principles and cloud computing platforms such as Azure, AWS, and GCP. Familiarity with data architecture, data warehouse models, Kimball methodology, dimensional modelling, and the Data Vault approach is essential. The successful candidate will be ISTQB certified and have insurance domain and cloud certifications, bringing valuable domain knowledge in the insurance industry while collaborating with cross-functional teams to define testing standards, reduce quality costs, and ensure data integrity at scale.
Mandatory Skills:
Proven expertise in Data Warehouse Quality Assurance using cloud platforms such as Azure, AWS, Databricks, PySpark.
Strong data analysis capabilities, with the ability to interpret and validate complex datasets using a variety of data analysis tools to interpret and report on data
Experience in defining and implementing testing standards, methodologies, and best practices across cross-functional teams.
Hands-on proficiency in both structured and unstructured databases Spark-SQL, MongoDB, Unix, Oracle, Redshift, and PySpark.
Solid understanding of data modeling, SQL querying and data pipeline design.
Ability to comprehend and translate complex business requirements into effective testing strategies.
Practical experience working within an Agile development environment.
Familiarity with the Atlassian toolset, including JIRA, Confluence, Bitbucket, Jenkins, and Git.
In-depth insurance domain knowledge, with a focus on data-centric applications.
Strong understanding of data modeling techniques, including conceptual, logical, and physical data models
Experience in cloud data migration projects involving insurance COTS products such as Guidewire and Evolve
Experience in validating ML models, automating testing pipelines, and ensuring model robustness and fairness. Skilled in Python, PyTest, MLflow, and CI/CD tools using Jenkins and Docker.
Certified in ISTQB, Azure Cloud, and insurance domains
Duties and Responsibilities:
Prepare and own the end-to-end test strategy for Data Warehouse programs
Liaison with BSA to understand the requirements
Create / update the Requirements traceability matrix with test scenarios.
Create/Modify the SQL test scripts as per the ETL mapping documents and technical specifications & Requirements.
Run the jobs and perform smoke testing
Record and maintain testing evidence and track execution status of assigned test cases.
Analyze test results and defect data to identify trends and root causes.
Use data visualization tools (e.g., Power BI, Tableau, Python libraries) to present testing metrics and KPIs.
Develop predictive models to forecast defect density, test coverage gaps, and release readiness.
Perform exploratory data analysis (EDA) on production and test data to uncover quality issues
Identify defects and log failures.
Track defects (defect log as generated from the QC tool/JIRA Tool) to closure.
Participate in the defect triages to gather evidence for defect identification on periodic basis for defect prioritization and fix.
Internal review and share for stakeholder review and feedback. Collaborate closely with BA & development team
Ensure all test data aligns with privacy and compliance requirements across test environments
Lead the development of automated test suites for functional, performance, and security testing.
Design and implement automated testing pipelines for ML models using PyTest and MLflow.
Integrate test automation with CI/CD pipelines.
Monitor and maintain test environments and data integrity.
Qualifications & Certifications (Optional):
Bachelor of Engineering
Azure DP-900 certification (or any equivalent)
ISTQB certification
Insurance domain certification-LOMA 280 & 290 certified for insurance domain (or any equivalent).
Understanding of mining tools to automate turning unstructured text into structured data
Salary Range: >$100,000
Date of Posting: 31/July/2026
Next Steps: If you feel this opportunity suits you, or Cognizant is the type of organization you would like to join, we want to have a conversation with you! Please apply directly with us.
For a complete list of open opportunities with Cognizant, visit http://www.cognizant.com/careers. Cognizant is committed to providing Equal Employment Opportunities. Successful candidates will be required to undergo a background check.
#LI-CTSAPAC
💡 Quick Summary
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