SDET II (NextGen)

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.