QA Director

About Vista Equity Partners

Vista Equity Partners is a leading global investment firm focused exclusively on enterprise software, data, and technology-enabled businesses. With over $100B in assets under management and a portfolio of 90+ software product companies worldwide, Vista accelerates growth through operational excellence, shared expertise, and long-term partnership. In India, Vista’s presence continues to expand with 45+ portfolio companies employing more than 17,000 professionals across technology, product, customer success, and operations — reinforcing India’s strategic role as a hub of innovation and talent within the Vista ecosystem.

 

Through its Agentic AI Factory, Vista is embedding Generative AI across its global portfolio — enabling companies to integrate intelligent, responsible AI into products, operations, and decision-making. This initiative is strengthened through portfolio-wide learning programs, leadership workshops, and AI hackathons that foster innovation, build fluency, and accelerate practical AI adoption across teams.

About Amtech

Amtech is a leading provider of enterprise software solutions for the packaging, printing, and manufacturing industries. Our integrated systems streamline order management, production planning, scheduling, inventory, and business analytics — empowering customers to drive efficiency, reduce costs, and improve operational performance. With a strong commitment to innovation and customer success, Amtech delivers reliable technology backed by deep industry expertise.

With Vista’s investment and strategic guidance, we combine the agility of a growing technology organization with the scale, stability, and career mobility of a global software ecosystem.

 

Role Description

The Director of Quality Engineering is accountable for modernizing and scaling quality across ERP, SaaS, Cloud, and AI/Data platforms as the company transitions to a multi-tenant SaaS architecture. 

 

This is not a legacy QA role. The QE Director builds an engineering-driven quality ecosystem—automation-first, cloud-native, AI-enabled, and deeply integrated into CI/CD and production observability. 

 

You will define strategy, build the QE operating model, lead platform and embedded QE teams, and partner tightly with Engineering, Product, SRE, Security, and Data to ensure speed without regression and innovation without instability. 

 

Key Outcomes (What Success Looks Like) 

• Manual regression is largely eliminated and replaced with scalable automation
• Quality is embedded early (“shift left”) and validated continuously through production
• ERP, SaaS, Cloud, and AI/Data platforms ship faster with fewer escaped defects
• Engineering teams own quality with QE acting as an enabler, not a gatekeeper
• Production quality signals are visible, measurable, and actionable

 

Key Responsibilities

1. Quality Engineering Strategy & Leadership

• Own and evolve the enterprise Quality Engineering strategy aligned to SaaS modernization 
• Establish QE as an engineering discipline, not a testing function 
• Define and enforce quality standards, metrics, and Definition of Done across teams 
• Serve as the executive voice of quality for Product, Engineering, and Leadership 

1. QE Operating Model & Team Structure

• Build and lead a lean, high-impact QE organization, including: 
• QE Platform & Tooling team 
• Embedded SDETs aligned to product squads 
• Production Quality Engineering in partnership with SRE 
• Hire, mentor, and develop senior QE leaders, architects, and SDETs 
• Scale globally while maintaining consistency and financial discipline 

       3. ERP Quality Engineering 

• Modernize quality practices for enterprise ERP systems (custom and/or packaged) 
• Drive API-first, data-driven testing strategies for ERP integrations 
• Ensure reliability across complex workflows, financial data, and compliance-sensitive processes 
• Reduce regression risk during ERP modernization, upgrades, and cloud migration 

      4. SaaS & Multi-Tenant Quality Engineering 

• Define testing strategies for multi-tenant SaaS platforms, including: 
• Tenant isolation 
• Data partitioning and security 
• Upgrade and backward compatibility validation 
• Ensure API, contract, and minimal UI automation cover critical customer journeys 
• Implement synthetic monitoring and canary validation in production 

    5. Cloud & Platform Quality Engineering 

• Embed quality validation into cloud-native architectures (Kubernetes, containers, IaC) 
• Drive load, stress, and scalability testing aligned to cloud-native architectures 
• Drive automation for: 
• Environment provisioning 
• Ephemeral test environments 
• Infrastructure and configuration validation 
• Partner with SRE on resilience, performance, chaos testing, and observability-driven quality 

 6. AI & Data Quality Engineering 

• Define quality standards for AI/ML models, data pipelines, and analytics platforms 
• Ensure data accuracy, schema stability, and pipeline reliability 
• Leverage AI to: 
  • Accelerate test generation and coverage analysis 
  • Detect flakiness and production anomalies 
  • Optimize regression selection and CI efficiency 
  • Partner with Data Engineering and Security to validate data privacy, bias, and integrity 

  7. Automation, CI/CD & Production Validation 

• Own automation frameworks for unit, API, contract, UI, performance, security, and resilience testing 
• Integrate continuous testing into CI/CD pipelines 
• Establish production quality validation using: 
• Synthetic transactions 
• Canary releases 
• Real-time telemetry and SLO monitoring 

 8. Metrics, Governance & Continuous Improvement 

• Define and track actionable quality metrics, including: 
• Defect escape rates 
• Automation coverage 
• Pipeline stability and flakiness 
• Performance and reliability SLOs 
• Use data—not intuition—to drive quality decisions 
•  Run regular quality reviews with engineering and product leadership 
• Define and track performance SLOs, including: 
  • Response time percentiles (p95/p99) 
  • Throughput and saturation thresholds 
  • Error rates under load 

Skills and Qualifications

• 12+ years in Quality Engineering, Software Engineering, or SDET leadership roles 
• Proven experience modernizing quality for ERP, SaaS, and Cloud platforms 
• Strong background in automation-first testing strategies 
• Hands-on understanding of CI/CD, cloud infrastructure, and microservices 
• Experience leading globally distributed engineering or QE teams 
• Track record of influencing senior engineering and product leaders 

 

Technical Expectations (Director-Level Fluency Required) 

• API and contract testing strategies 
• Cloud-native architectures (containers, Kubernetes, IaC) 
• CI/CD pipelines and DevOps practices 
• Performance, security, and resilience testing 
• Experience conducting 'Game Days' or Chaos Engineering experiments to validate system behavior under failure conditions—ensuring the multi-tenant platform is self-healing [Resilience Testing] 
• Observability and production monitoring 
• AI-assisted testing and data quality concepts 
• Experience managing the infrastructure of ROI and cloud spend associated with large-scale automation and performance environments. 
• Expertise in complex data migration workflows during ERP-to-Cloud transitions 
• Fluency in DevSecOps practices, including the automation of security gates (SAST/DAST). 
• Influence without Authority - Demonstrated success in shifting organizational culture where software developers take primary ownership of lower-level automation, supported by QE frameworks. 

Why Join Amtech

At Amtech, you will drive meaningful financial impact in a growing enterprise software organization while benefiting from Vista’s world-class ecosystem. You’ll collaborate with talented peers, leverage cross-portfolio learning programs, and help shape the future of Amtech’s financial operations and systems. Build your career with Amtech — backed by the strength, scale, and innovation culture of Vista.