The right candidate has deep quality engineering experience, with a proven track record of owning quality strategy across complex SaaS platforms. You have moved well beyond execution — you build systems of quality, mentor Senior and Lead QEs, and drive engineering culture. "No defect escaped on my watch" was a personal standard earlier in your career. At this level, it is an organizational outcome you are responsible for designing.
Responsibilities
Quality Ownership & Test Execution
Own quality outcomes for the Reporting/BI domain across multiple scrum teams — not as a reviewer of output, but as the architect of how quality is built into every stage of delivery, from requirements through production.
Define platform-wide test strategy and coverage standards for reporting features, SQL and PL/SQL changes, BI enhancements, and data pipeline work — spanning functional correctness, financial data accuracy, aggregation logic, hierarchical traversal, performance, regression, security, and non-functional scenarios.
Establish and own the test condition framework derived from user stories, reporting specifications, and data contracts — setting the standard for what constitutes complete coverage, including boundary conditions, null handling, edge cases, and audit-trail accuracy.
Hold accountability for defect resolution at the domain level — not just identification. Partner with Engineering Leads, Product Management, and DevOps to ensure defects are triaged, prioritized, and closed with root cause addressed, not just symptom-fixed.
Represent quality at quarter planning, release governance, and cross-team sprint ceremonies — with the authority to flag coverage gaps, challenge scope, and influence delivery decisions before commitments are made.
Set and enforce consistent quality standards across all Reporting/BI scrum teams in the India CoE, partnering with the VP/Director of QA & Standards and Engineering Directors to ensure platform-level coherence, not team-by-team variation.
SQL & Data Accuracy Validation
Own the SQL and PL/SQL validation architecture for the Reporting/BI domain — designing the reusable query libraries, validation patterns, and data reconciliation frameworks that the QE team operates across all features, releases, and client environments. Writing SQL to validate report output is the baseline; owning how that validation scales is the job.
Define and govern data accuracy test strategy for complex, non-flattened hierarchical data models — establishing standards for how reporting queries are validated across hierarchy traversals, rollup levels, aggregation logic, financial calculations, and referential integrity — and ensuring those standards are applied consistently across all QEs on the domain.
Lead validation of SQL and PL/SQL changes — stored procedures, packages, views, and query modifications — setting the review standard for correctness, performance characteristics, and side-effect analysis, and conducting deep-dive reviews on high-risk or high-complexity changes personally.
Set the bar for defect evidence quality — establishing team-wide standards for how data discrepancies are isolated, documented, and communicated to engineers, such that SQL evidence pinpoints the exact point of failure in the data pipeline without ambiguity or back-and-forth.
Own query performance benchmarking as a platform-level quality gate — defining acceptable thresholds, maintaining baseline metrics across report types and data volumes, and ensuring performance validation is a non-negotiable part of the definition of done for every SQL change that ships.
Reporting & BI Output Testing
Define and own the end-to-end report output fidelity standard for the CEM platform — establishing what "correct" means across rendered output, totals, formatting, filtering behavior, and drill-down results, and ensuring that standard is applied consistently across all QEs, all report types, and all client environments.
Set the validation framework for Logi Analytics (Logi Symphony) implementations across the domain — governing how report rendering, parameter handling, data binding, conditional logic, and export output are tested, and ensuring coverage patterns are reusable and not rebuilt from scratch for each feature.
Own the parameterization and configuration test strategy — defining combinatorial coverage models for user-selectable filters, date ranges, grouping options, and report variants that give the team confidence without requiring exhaustive manual execution on every release.
Hold accountability for financial report output accuracy across regulated enterprise client environments — setting the quality bar for auditability, compliance-readiness, and data correctness at a level that reflects the compliance implications of errors in financial reporting output, and ensuring the entire QE team operates to that bar, not just flags issues when they appear.
Own report performance validation as a platform quality gate — defining load thresholds by report type and data volume, maintaining regression baselines across releases, and driving engineering response when performance degradation is detected rather than simply logging it.
AI-Augmented Testing
Define and own the AI tooling adoption strategy for the QA & Standards organization — establishing how AI is used across test plan generation, edge case identification, SQL validation authoring, data synthesis, and end-to-end scenario coverage, and setting the standards by which AI-generated artifacts are reviewed, trusted, and promoted into production pipelines.
Lead by example in AI-augmented quality practice — using AI tooling to achieve coverage depth and scenario breadth that manual effort alone cannot reach, and making that the expected baseline for every QE on the domain, not a differentiator for a few.
Drive continuous improvement of AI tooling effectiveness across the team — evaluating output quality, identifying failure modes, and evolving prompting strategies, toolchain integrations, and review workflows so that AI adoption raises the quality bar rather than creating a false sense of coverage.
Own forward-looking AI tooling evaluation for the QA organization — assessing emerging capabilities in test generation, intelligent triage, and data synthesis, running structured pilots, and delivering evidence-based adoption recommendations to engineering and QA leadership.
Automation Development & Maintenance
Own the automation investment strategy for the Reporting/BI domain — deciding what gets automated, in what order, to what coverage threshold, and using what tooling, with SQL-based data validation and API test coverage as the primary surfaces and UI automation secondary.
Architect and govern the shared automation framework — defining SQL assertion patterns, API test structure, CI/CD integration standards via Azure DevOps, and the review criteria by which automated test contributions from QEs across the team are accepted or rejected.
Set and enforce the decision framework for automation vs. manual testing — establishing principles around coverage value, maintenance cost, and delivery velocity that give the team consistent, defensible guidance rather than case-by-case judgment calls.
Own automation pipeline health at the domain level — defining failure triage SLAs, distinguishing genuine defects from environmental noise at scale, and driving continuous improvement in coverage reliability and signal-to-noise ratio across all CI/CD-integrated test suites.
Security Testing
Define the security testing standard for the Reporting/BI domain — establishing what security coverage is required across input validation, authentication and authorization boundaries, data exposure risks, and injection vulnerabilities in report parameters and API inputs, and ensuring it is built into every QE's test approach, not treated as an afterthought.
Engage at the design and architecture stage to identify security-sensitive surfaces in new reporting features — influencing implementation decisions that affect testability before build begins, not after.
Lead AI-augmented security scenario generation for the domain — using tooling to surface edge cases and attack vectors that manual analysis would miss, and establishing standards for how AI-generated security test artifacts are validated before use.
Own security risk escalation for the domain — assessing severity, framing impact in terms of compliance and client exposure, and driving resolution with Engineering and QA leadership rather than simply flagging and moving on.
Collaboration & Communication
Operate as a peer-level engineering partner to Lead Engineers and Engineering Managers — embedded at design and architecture reviews, not just sprint ceremonies, with the standing to influence implementation decisions that affect quality and testability before they are locked in.
Set the quality communication standard for the domain — defining how defect status, test coverage, data accuracy findings, and quality risk are reported to scrum teams, QA leadership, and Engineering Directors, and ensuring QEs across the team meet that standard consistently.
Lead mentorship and talent development for Senior and Lead QEs — conducting structured test and code reviews, establishing SQL validation and automation coaching programs, and actively contributing to IDP conversations and career growth within the QA & Standards organization.
Serve as the primary quality interface for enterprise client stakeholders — owning defect evidence documentation, issue clarification, and quality assurance communications for UBS, Bank of America, and other strategic engagements directly, without requiring direction from the Lead Engineer.
Qualifications
Required
9+ years of software quality engineering experience in enterprise SaaS environments, with at least 3+ years in a Senior or Lead QE capacity and demonstrable progression into domain-level quality ownership.
Proven track record of owning quality outcomes at platform or product level — not just driving defects to resolution, but designing the systems, standards, and processes that prevent escapes at scale.
Expert-level Oracle SQL and PL/SQL — complex joins, window functions, CTEs, recursive queries, analytical functions, explain plan analysis, and performance tuning. Designing SQL validation frameworks others operate, not just writing queries yourself.
Deep experience owning test strategy for reporting or BI systems — report output fidelity, data transformation correctness, aggregation logic, and analytical query validation against source data across multiple teams or release streams.
Demonstrated ability to define and govern test strategy at domain level — coverage standards, definition of done criteria, and test architecture decisions that apply across scrum teams, not within one.
Established AI tooling practice in quality engineering — having defined adoption standards, evaluated output quality, and driven team-wide usage across test generation, SQL authoring, security scenarios, and data synthesis.
Strong proficiency in C# and .NET test frameworks (NUnit, xUnit, or MSTest) — with experience architecting and governing shared automation frameworks, not just contributing to them.
Security testing ownership — setting domain-level security test standards covering input validation, auth boundary testing, injection vulnerabilities, and OWASP-aligned coverage, and ensuring QEs across the team apply them consistently.
Expert-level command of JIRA and Xray — including test governance, coverage reporting, and quality metrics at the program or domain level.
Experience representing quality at PI planning, release governance, and cross-functional architectural forums — with the standing to influence delivery decisions, not just report on quality status.
Demonstrated experience mentoring Senior and Lead QEs — structured coaching, test and code reviews, and active contribution to career development and IDP conversations.
Strong executive communication skills — able to frame quality risk, coverage status, and data accuracy findings for Engineering Directors, Product leadership, and enterprise client stakeholders without translation.
Bachelor's degree in Computer Science, Engineering, or equivalent experience at a level consistent with the scope of this role.
Preferred
Deep domain expertise in financial reporting, equity compensation, financial services, or regulated environments where report accuracy carries compliance and audit implications — this is a significant differentiator at this level.
Direct hands-on experience architecting validation coverage for Logi Analytics (Logi Symphony) or comparable enterprise BI/embedded analytics platforms — at a depth sufficient to define the team's testing standard for the platform, not just execute against it.
Expert-level command of hierarchical and tree-structured data model validation — establishing coverage patterns for parent/child relationships, rollup correctness, and drill-down accuracy that other QEs can apply reliably at scale.
Familiarity with BI tooling beyond Logi — Power BI, Tableau, Cognos, or similar — sufficient to evaluate cross-platform validation approaches and inform tool selection decisions.
Deep familiarity with Oracle read-optimization patterns — materialized views, result cache, analytical functions — sufficient to make architectural recommendations on query performance under test at enterprise data volumes.
Experience with Datadog or equivalent observability platforms — using logs, traces, and error events to diagnose test failures and drive root cause investigations across distributed systems.
Hands-on experience with Azure DevOps Pipelines for test execution governance, environment management strategy, and CI/CD quality gate design.
Familiarity with Azure cloud data services relevant to reporting — Azure SQL, Azure Analysis Services, or similar — at a level sufficient to inform test environment and data state requirements.
Breadth across modern test automation tooling — Playwright, Cypress, k6, SpecFlow, or similar — sufficient to make informed framework and tooling decisions for the domain.
Additional Information
All your information will be kept confidential according to EEO guidelines.
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