SDET II (NextGen)

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:

insightsoftware

Equity Management Engineering — Job Description

Senior Quality Engineer — Reporting/BI Engineering

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

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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.

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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.

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

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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.

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

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

SQL & Data Accuracy Validation

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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.

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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.

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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.

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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.

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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

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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.

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

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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.

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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.

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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

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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.

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

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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.

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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

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

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

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