Quality Engineering Lead-Data, AI & Testing

Mastek; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Mastek seeks a Quality Engineering Lead to govern testing across life sciences IT programs, vendors, and data platforms. You will shape risk-based test strategies for traditional apps, data pipelines and AI/GenAI solutions, ensuring CSV/CSA and 21 CFR Part 11 compliance and traceability.

You will lead QA across Databricks and AWS data environments, drive data quality, validation, and test automation with AI-assisted approaches, collaborating with SMEs and vendor leadership.

Qualifications

• 10–15 years of software testing/Quality Engineering experience, including enterprise leadership.
• Experience developing risk-based testing strategies aligned with business, technical, and compliance needs.
• Proven ability to govern testing across multiple IT teams and vendors.
• Experience with data platforms, data pipelines, and data quality frameworks.
• Hands-on testing of AI/ML/GenAI solutions, including evaluation metrics and grounding checks.

Responsibilities

• Define and govern risk-based test strategies across programs and vendors.
• Review test strategies, risk coverage, defects, and release-readiness decisions.
• Set up standard testing processes, templates, quality gates, metrics and reporting.
• Lead data quality engineering for ingestion, transformation and integration.
• Define testing for AI/GenAI, including prompts, bias, and model monitoring.
• Apply AI to testing with AI-assisted design, data generation and defect analysis.

Skills

Test strategy

Risk-based testing

Data quality engineering

AI/GenAI testing

SQL proficiency

Stakeholder management

Vendors management

GxP & CSV awareness

Tools

Jira

Azure DevOps

ALM

qTest

Tosca

Selenium

Databricks

AWS

Great Expectations

dbt tests

Unity Catalog lineage

Job description

Quality Engineering Lead – Data, AI and Testing (Life Sciences experience mandatory)

About the role

Mastek is looking for a Quality Engineering Lead to set and govern testing standards across clients IT programs, vendors, applications and business functions. As client puts more of its work on cloud data platforms and AI, the role covers both traditional application testing and the quality of data pipelines, analytics and AI-enabled solutions. All of it must meet life sciences’ regulatory expectations.

What you do

• Define and govern risk-based test strategies across programs and vendors. Testing depth should match business, technical, compliance and patient-safety risk.
• Review and challenge test strategies, test cases, risk coverage, defect management and release-readiness decisions.
• Set up standard testing processes, templates, quality gates, metrics and reporting across programs.
• Lead data quality engineering. Define testing for data ingestion, transformation and integration, including data validation, reconciliation, completeness, lineage and data contracts, on platforms such as Databricks and AWS.
• Set the approach for testing AI and GenAI solutions. Cover accuracy and grounding of outputs, prompt and regression test sets, bias and robustness checks, guardrails, human-in-the-loop review, and monitoring of model behavior after release.
• Apply AI to testing itself. Use AI-assisted test design, test-data generation, automation and defect analysis to speed up delivery without weakening control.
• Make sure GxP-relevant systems, including data products and AI-enabled workflows, meet CSV/CSA, 21 CFR Part 11, traceability and test-evidence requirements.
• Work with business SMEs, IT teams, Quality/Compliance and vendor leadership. Bring in specialists (Clinical, Regulatory, Quality, Commercial, Finance) when needed.

Must-have

• 10–15 years of software testing/Quality Engineering experience, including enterprise-level test leadership.
• Strong expertise in test strategy, test planning, boundary-value analysis, negative testing, regression, integration, and end-to-end testing.
• Demonstrated experience developing and reviewing risk-based testing strategies, ensuring that testing depth and coverage are aligned with business, technical, compliance, and patient-safety risks.
• Experience governing testing across multiple IT teams, applications, and third-party vendors.
• Ability to review and challenge test strategies, test cases, risk coverage, defect management, and release-readiness decisions.
• Experience establishing standardized testing processes, templates, quality gates, metrics, and reporting across programs.
• Ability to understand business processes and ensure adequate coverage of functional scenarios, controls, integrations, data flows, exceptions, and high-risk areas.
• Hands-on experience testing data platforms and pipelines (ETL/ELT, data warehouses or lakehouses), including data validation, reconciliation and data quality frameworks. SQL proficiency is expected.
• Working understanding of how AI/ML and GenAI solutions are tested and validated:
• evaluation metrics and test sets
• hallucination and grounding checks
• model and prompt versioning
• risk-based controls for AI in regulated settings
• High-level understanding of the life sciences domain and regulated application environments.
• Working knowledge of Computer System Validation (CSV), Computer Software Assurance (CSA), GxP, 21 CFR Part 11, traceability, and test-evidence requirements.
• Strong stakeholder-management skills and the ability to work with business SMEs, IT teams, Quality/Compliance groups, and vendor leadership.
• Ability to identify when specialized expertise—such as Finance, Clinical, Quality, Regulatory, or Commercial—is required and engage the appropriate SMEs.

Good to have

• High-level understanding of GAMP 5 principles and their application to risk-based validation and testing, including the GAMP guidance on AI/ML-enabled computerized systems.
• Experience across life sciences functions such as Clinical, Commercial, Quality, Finance, Supply Chain, or Regulatory.
• Exposure to enterprise platforms such as Salesforce, Veeva, Oracle, SAP, data platforms, or other cloud applications.
• Exposure to Databricks, AWS data services or Amazon Bedrock, and to data-quality or observability tools (for example Great Expectations, dbt tests or Unity Catalog lineage).
• Familiarity with emerging AI regulation and guidance, such as the EU AI Act and FDA thinking on AI in regulated software.
• Experience with test-management and automation tools such as Jira, Azure DevOps, ALM, qTest, Tosca, Selenium, or similar tools, including AI-assisted testing tools.
• ISTQB, CSTE, CSQA, or another relevant testing or quality certification. An AI testing certification such as ISTQB CT-AI is a plus.