Principal Software Development Engineer in Test

About Us

insightsoftware is a global provider of reporting, analytics, and performance management solutions that unlock the potential of business data and transform the way finance and data teams operate. We empower leaders from over 32,000 organizations to make timely and intelligent decisions. Our comprehensive solutions span Financial Planning and Analysis (FP&A), Controllership, and Data and Analytics. We deliver finance teams the insights required to navigate any economic climate and drive greater financial intelligence, while increasing productivity, visibility, accuracy, and compliance. Learn more at insightsoftware.com.

Job Description:

Job Description

As a Principal Quality Engineer embedded in the Reporting/BI Engineering domain, you will serve as the quality authority for the Certent Equity Management (CEM) platform — operating across multiple scrum teams. You will report into the QA & Standards organization while partnering directly with Engineering Directors, Lead Engineers, and Product Management to define and enforce quality outcomes at the platform level. This role carries named accountability for Go/No-Go decisions on major releases and is a peer-level voice in architectural and delivery planning.

This is not an application-layer QA role. The Reporting/BI test surface is dominated by SQL correctness, data accuracy, query performance, and report output fidelity at financial detail level — and you will own the architecture of how that validation is done, not just execute it. You design the SQL validation framework others operate, set API automation strategy, and define the coverage standards the team is held to. AI is a core part of how you work, and you are expected to lead its adoption across the QE organization.

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.

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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.

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