SDET II (NextGen) at insightsoftware in India - Hyderabad - Remote

Quality Ownership & Test Execution

• Own the quality of the Reporting/BI team’s deliverables — from requirements review through release — ensuring nothing ships without adequate test coverage across functional, data accuracy, performance, and non-functional scenarios.

• Define, document, and execute test plans and test cases for new reporting features, SQL changes, BI enhancements, and bug fixes — covering report output correctness, data transformation accuracy, edge cases, regression, security, and non-functional scenarios.

• Identify test conditions from user stories, reporting specifications, and requirements documents — including positive, negative, boundary, hierarchical data traversal, aggregation correctness, and null handling scenarios.

• Execute test cases, document results, track defects, and own them through to resolution — partnering with engineers and product management to ensure nothing falls through the cracks. Identifying a problem is the beginning, not the end.

• Participate actively in sprint ceremonies — planning, refinement, standups, demos, and retrospectives — as the quality voice of the team.

• Collaborate with the Principal QA Lead and Sr Engineering Manager — QA & Standards to maintain consistent quality standards across scrum teams.

SQL & Data Accuracy Validation

• Write Oracle SQL and PL/SQL queries to validate report output against source data — verifying row counts, field-level accuracy, aggregation correctness, financial calculations, and referential integrity. This is the primary testing tool for this team.

• Design and execute data accuracy test strategies for complex, non-flattened hierarchical data models — validating that reporting queries traverse hierarchies correctly and produce accurate results across all nodes and rollup levels.

• Validate SQL and PL/SQL changes — stored procedures, packages, views, and query modifications — ensuring correctness, expected performance characteristics, and no unintended side effects on existing report output.

• Identify and document data discrepancies clearly — providing engineers with precise SQL evidence that isolates where in the data pipeline a calculation or transformation is producing incorrect results.

• Validate query performance benchmarks as part of definition of done — confirming that new or modified queries meet the team’s performance standards and do not introduce regression in report responsiveness.

Reporting & BI Output Testing

• Test end-to-end report output fidelity — validating that what is displayed in a report or BI dashboard accurately reflects the underlying data, with correct formatting, correct totals, correct filtering behavior, and correct drill-down results.

• Validate Logi Analytics (Logi Symphony) report and dashboard implementations — testing report rendering, parameter handling, data binding, conditional logic, and export output against expected data.

• Test report configuration and parameterization — verifying that user-selectable filters, date ranges, grouping options, and report variants produce correct, consistent results across all input combinations.

• Validate financial report output for accuracy and auditability — understanding that errors in financial reporting output for a regulated enterprise client carry compliance implications, and treating data correctness accordingly.

• Test report performance — validating that reports load within acceptable thresholds under representative data volumes, and flagging regressions in report responsiveness for investigation.

AI-Augmented Testing

• Leverage AI tooling to generate test plans, test cases, edge cases, positive and negative scenarios, end-to-end scenarios, security scenarios, and test data sets — expanding coverage and accelerating test authoring.

• Use AI tooling to accelerate the authoring of SQL validation queries, data comparison scripts, and report output test cases.

• Continuously improve your use of AI tooling to raise the quality bar — using AI not just to work faster but to test more thoroughly than manual effort alone could achieve.

• Stay current on emerging AI tooling relevant to QA — test generation, intelligent triage, data synthesis — and bring forward-looking recommendations to the Principal QA Lead and Sr Engineering Manager — QA & Standards.

Automation Development & Maintenance

• Write and maintain automated test suites for the features and SQL changes you test — owning automation as an extension of your manual testing work.

• Prioritize SQL-based data validation automation and API test coverage as the primary automation surfaces for this team; UI automation is secondary.

• Make informed decisions on what to automate vs. what to test manually — balancing coverage value, maintenance cost, and delivery velocity.

• Contribute to the shared automation framework and test infrastructure alongside peer QA Engineers — including SQL assertion patterns, API test coverage, and CI/CD pipeline integration via Azure DevOps.

• Execute automated tests within CI/CD pipelines; triage failures to distinguish genuine defects from environmental noise.

• Continuously improve automation coverage, reliability, and triage efficiency within the Reporting/BI team.

Security Testing

• Incorporate security testing into your standard test approach — including input validation, authentication and authorization boundary testing, data exposure risks, and injection vulnerabilities in report parameters and API inputs.

• Partner with engineers to identify security-sensitive areas of new reporting features and ensure appropriate security test coverage is included in test plans.

• Leverage AI tooling to generate security-focused test scenarios and edge cases that manual analysis alone might miss.

• Escalate identified security risks clearly and promptly to the scrum team, Principal QA Lead, and Sr Engineering Manager — QA & Standards.

Collaboration & Communication

• Partner closely with the Lead Engineer and peer engineers to understand reporting implementations, SQL changes, and data model updates — flagging quality risks early and influencing design decisions that affect testability.

• Collaborate with the Principal QA Lead and Sr Engineering Manager — QA & Standards on test strategy, coverage standards, and process improvements.

• Communicate defect status, test progress, data accuracy findings, and quality risks clearly to the scrum team and QA leadership.

• Support junior QA Engineers through peer review, knowledge sharing on SQL-based data validation techniques, and mentorship.

• Interface with client stakeholders as needed under the direction of the Lead Engineer — reporting issue clarification and defect evidence documentation.

Qualifications

Required

• 6+ years of software quality engineering experience in enterprise SaaS environments.

• Demonstrated ownership mindset — a track record of driving defects to resolution rather than just logging them, and taking personal accountability for the quality of everything your team ships.

• Hands-on experience writing SQL to validate data accuracy at the database layer — row counts, field-level correctness, aggregation logic, and relational integrity. This is the primary required skill for this role; Oracle experience strongly preferred.

• Experience testing reporting or BI systems — validating report output fidelity, data transformation correctness, or analytical query results against source data.

• Strong manual testing expertise — ability to independently define test strategy, write thorough test plans and test cases, and execute across functional, data accuracy, and non-functional scenarios.

• Demonstrated experience using AI tooling to augment test authoring — test plan generation, test case generation, edge case identification, security scenario generation, or test data synthesis.

• Proficiency in C# and hands-on experience with .NET test frameworks (NUnit, xUnit, or MSTest) — proven ability to build and maintain automated test suites.

• Experience with security testing concepts and tooling — input validation, auth boundary testing, injection vulnerabilities, and familiarity with OWASP, Burp Suite, or similar.

• Experience with defect tracking and test management tooling — JIRA and Xray specifically, including test tagging, execution tracking, and result publishing.

• Experience working as an embedded QA engineer within agile/scrum teams — participating in sprint ceremonies and contributing to team delivery cadence.

• Experience working in global, distributed teams across multiple time zones.

• Strong written and verbal communication skills — able to write clear defect reports with SQL evidence, test documentation, and data accuracy findings.

• Bachelor’s degree in Computer Science, Engineering, or equivalent work experience.

Preferred

• Background in financial reporting, equity compensation, financial services, or adjacent regulated domains where report output accuracy and auditability are critical — strongly preferred.

• Direct hands-on experience with Logi Analytics (now Logi Symphony) or comparable enterprise BI/embedded analytics platforms — this is a significant differentiator.

• Deep Oracle SQL and PL/SQL proficiency — stored procedures, packages, views, execution plan analysis, and query performance tuning — at a level sufficient to independently investigate data discrepancies in complex relational data models.

• Experience validating hierarchical or tree-structured data models — understanding how non-flattened parent/child relationships affect aggregation, rollup, and drill-down correctness in reports.

• Familiarity with BI tooling beyond Logi — Power BI, Tableau, Cognos, or similar — with the ability to validate report output and data binding against source data.

• Experience with Oracle read-optimization patterns — materialized views, result cache, analytical functions — sufficient to understand the performance implications of query changes under test.

• Familiarity with observability and logging platforms — particularly Datadog — to diagnose failing test cases by correlating test execution failures with application logs, traces, and error events.

• Familiarity with Azure DevOps Pipelines for test execution, environment management, and CI/CD integration.

• Familiarity with Azure cloud data services relevant to reporting — Azure SQL, Azure Analysis Services, or similar.

• Familiarity with modern test automation tooling — Playwright, Cypress, k6, SpecFlow, or similar.