SDET

Reports To: Principal QA Lead or Sr Engineering Manager QA & Standards

Location: Remote (India) | Global team

Team: Reporting/BI Engineering — fully embedded

About insightsoftware

insightsoftware is a growing, dynamic computer software company that helps businesses achieve greater levels of financial intelligence across their organization with our world-class financial reporting solutions. At insightsoftware, you will learn and grow in a fast-paced, supportive environment that will take your career to the next level. We are looking for future Insighters who can demonstrate teamwork, results orientation, a growth mindset, disciplined execution, and a winning attitude to join our growing team!

Job Description

As a Senior Quality Engineer embedded in the Reporting/BI Engineering team, you will be the primary quality voice for a scrum team focused on building and enhancing the financial reporting and business intelligence capabilities of the Certent Equity Management (CEM) platform for a large, strategic enterprise client engagement. You will report into the QA & Standards organization while working day-to-day within the Reporting/BI scrum team, collaborating closely with the Lead Engineer, engineers, and product management throughout the delivery lifecycle.

This is not a standard application-layer QA role. The Reporting/BI test surface is dominated by SQL correctness, data accuracy, query performance, and report output fidelity — not UI workflows. You will need to think in terms of data: whether the right rows came back, whether the aggregations are correct, whether hierarchical traversal produces the right results, whether a report output matches the source data at the financial detail level. Writing SQL to validate data is not optional here — it is the primary tool for most of your testing.

The right candidate takes personal ownership of quality outcomes — not just identifying problems, but partnering with engineering to drive them to resolution. “No bugs made it to production on my watch” is not a goal, it’s a standard. This is a hybrid manual and automation role. SQL-based data validation and API test coverage are the priority automation surfaces; UI automation is secondary. AI will be a core part of how you work across every aspect of quality engineering.

Responsibilities:

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.

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.