Magnitude Software - insightsoftware
Website:
insightsoftware.com
Job details:
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
- 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.
Additional Information
All your information will be kept confidential according to EEO guidelines.
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