Senior Manager, Quality Engineering

The Role
We are looking for a Senior QA Engineering Lead who thinks like a product engineer, operates like a data scientist, and leads like a builder. This is not a traditional test
management role. This is a pivotal, senior leadership position within InvestCloud’s Digital Wealth product and technology group — responsible for transforming quality engineering from a gate at the end of the pipeline into an intelligent, continuous capability woven across every stage of the product delivery lifecycle (PDLC). You will lead a team of QA engineers while being deeply hands-on yourself — designing automation frameworks, embedding AI-driven quality intelligence, and setting the quality
engineering standard for one of the most complex, high-stakes product suites in the wealth management technology space.
You will work in close partnership with engineering, product, platform, data, and operations teams — driving a culture where quality is everyone’s responsibility and AI is
the accelerator.
What You Will Own
1. End-to-End Quality Strategy Across the PDLC
1. Define and own the quality engineering roadmap for Digital Wealth — from
requirements and design through development, integration, release, and production
2. Embed quality gates at every stage of the PDLC — shifting left to catch defects at design time, not post-release
3. Lead the evolution from manual, reactive testing to a predictive, AI-augmented quality model across all Digital Wealth product lines
4. Own the release confidence framework — Go/NoGo criteria, coverage thresholds,risk-based testing models, and rollback decision logic
2. Intelligent Test Automation & AI Enablement
5. Architect and own a modern test automation platform — functional, regression, API, performance, and BDD — integrated with CI/CD pipelines (GitLab)
6. Drive adoption of AI-powered QA tooling including: o AI test case generation from requirements (natural language → test suite)o Automated test taxonomy mapping and Jira classification (coverage gaps surfaced automatically) o Defect impact analysis — identifying which platform tiers, portals, andregression flows are affected by every fix o AI-assisted environment health monitoring — proactive alerting with module-level root-cause hints o AI-driven release notes and Go/NoGo documentation generated from live Jira + GitLab data
7. Champion test data management at scale — including AI-generated schema validated test datasets for complex scenario permutations (account × model × sleeve × restriction × security)
8. Integrate AI observability into QA — log analysis, anomaly detection, and predictive failure identification across Dev, SIT, CERT, and Production
environments
3. Quality Engineering Leadership & Team Development
9. Build, lead, and mentor a high-performing team of Quality Engineers and SDET engineers.
10. Shift the team identity from “testers” to Quality Engineers — full-stack capable, AI literate, and product-accountable
11. Define clear career paths, competency frameworks, and upskilling standards that prioritize hands-on engineering depth alongside functional wealth domain
knowledge
12. Foster a culture of shared quality ownership — coaching developers, BAs, and product managers to participate actively in quality practices
13. Partner with the engineering leadership team to define quality KPIs, sprint-level quality metrics, and release health dashboards visible to all stakeholders
4. Cross-Functional Integration & Release Intelligence
14. Serve as the quality owner across Digital Wealth’s cross-functional delivery squads — advisors, portfolio management, client reporting, operations, and settlements
15. Partner with Release Management to automate and accelerate the Go/NoGo release process — reducing release note preparation from hours to minutes
16. Integrate QA practices with SWIFT operations, reconciliation, and settlement workflows — ensuring quality extends to data accuracy and operational
correctness, not just functional behaviour
17. Own the defect lifecycle — from triage and root-cause classification through resolution verification and regression prevention
18. Drive environment health and readiness standards across all non-production tiers — ensuring Dev, SIT, CERT, and UAT environments are consistently stable and
observable
5. Metrics, Reporting & Continuous Improvement
19. Define and publish quality health metrics across the PDLC: defect escape rate,automation coverage, test execution velocity, environment stability, release risk
scores
20. Use data-driven quality insights to influence product prioritization, technical debt decisions, and engineering investment
21. Build a living quality knowledge base (integrated with Glean) — runbooks,regression taxonomies, test scenario libraries — that grows with every sprint and
survives team changes
22. Establish AI-driven regression intelligence — predictive models that identify which areas of the product are highest risk for a given release based on change
impact analysis
Key Skills We Are Looking For
Core Engineering (Must Have)
23. 10+ years of progressive QA / SDET / quality engineering experience in complex, enterprise SaaS or fintech product environments
24. Hands-on expertise in test automation frameworks — Selenium, Playwright, Cypress, RestAssured, or equivalent — with a strong preference for engineers who
have built frameworks from scratch, not just maintained them
25. Strong API testing expertise — REST, GraphQL, SOAP — with experience integrating API test suites into CI/CD pipelines
26. Proficiency in at least one programming language — Python, Java, C#, orJavaScript/TypeScript — at a level where you can write automation code, review
PRs, and debug failures independently
27. Deep experience with CI/CD integration — GitLab CI, Jenkins, or Azure DevOps —and quality gates in automated deployment pipelines
28. Solid understanding of performance and load testing — JMeter, k6, Gatling, orequivalent — and experience defining NFR baselines for wealth management
platformsAI & Intelligent Quality (Strong Preference)
29. Hands-on experience with AI-powered QA tooling — test generation fromrequirements, defect classification, impact analysis, or log intelligence
30. Familiarity with LLM-based automation — Claude, GPT-4, or similar — forgenerating test cases, interpreting logs, and producing release documentation
31. Experience with RAG-based knowledge retrieval for QA knowledge bases —runbooks, regression taxonomies, scenario libraries
32. Understanding of Glean, Jira, Confluence, and GitLab integrations for AI-assisted QA workflows
33. Ability to evaluate, adopt, and govern emerging AI QA tools responsibly —balancing innovation velocity with data security and compliance
Domain & Product (Preferred)
34. Experience testing wealth management, financial services, or capital markets platforms — portfolio management, advisor tools, settlements, reconciliation, or
client reporting
35. Familiarity with portfolio accounting systems, DataLoader patterns, or complex financial data models
36. Understanding of SWIFT messaging, trade settlement, or reconciliationworkflows from a quality and data accuracy perspective
37. Experience with regulatory compliance testing — ensuring products meetfinancial services governance and audit standards
Leadership & Culture (Essential)
38. Demonstrated ability to lead and grow QA engineering teams — hiring, mentoring, performance management, and capability development
39. Experience shifting quality left — embedding QA practices at requirements and design stages, not just execution
40. Strong stakeholder communication — ability to translate quality metrics, release risk, and defect trends into clear narratives for product, engineering, and senior
leadership audiences
41. Experience driving quality culture change — moving organizations from siloed testing to shared engineering quality ownership Comfortable operating in a senior individual contributor + people leader hybrid model —leading the team while remaining hands-on in architecture, tooling, and delivery

Location & Working Model
Bengaluru, India · 3-day in-office (hybrid) · Full-time permanent
InvestCloud India · Bengaluru · May 2026 · Confidential — For Internal Recruitment Use
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