QA Engineer 2 - DPP

We are looking for a QA Engineer to support the quality, reliability, and integrity of DPP deliverables across data ingestion, transformation, APIs, and downstream reporting. The successful candidate will review requirements and acceptance criteria, design and execute manual and automated tests, validate end-to-end data flows and business rules, and work closely with Product, Engineering, and Data teams to identify defects early, reduce delivery risk, and support high-quality releases.

Test Planning & Design

Review requirements, acceptance criteria, and supporting documentation to create clear, traceable test scenarios and test cases for each User Story.

Review test coverage with relevant stakeholders and maintain the regression suite to ensure ongoing support for business-critical DPP functionality.

Test Execution & Defect Management

Execute manual and automated tests to validate new features, regression scenarios, and data transformations across the DPP platform.

Record defects and data issues with clear reproduction steps, supporting evidence, and impact assessment to enable efficient triage and resolution.

Work closely with Engineering and Data teams to support root-cause analysis, validate fixes, and confirm production readiness.

Validation & Closure

Retest resolved defects and confirm that acceptance criteria, business rules, and quality expectations have been met before closure.

Maintain accurate defect reporting, status tracking, and quality documentation to support transparent delivery decisions.

Data Integrity & ETL Validation

Validate the accuracy, completeness, and consistency of extracted, transformed, and loaded data across the DPP platform.

Confirm that processed data aligns with business rules, acceptance criteria, and reporting expectations.

Continuous Improvement

Identify opportunities to improve QA processes, tooling, and automation to increase efficiency and test effectiveness.

Contribute to knowledge sharing and continuous learning within the wider QA and delivery teams.

Mandatory Tools:

Azure DevOps (Test Management & Release Support): Plan and execute DPP test cycles, manage test evidence and defects, support release readiness, and contribute to quality sign-off across delivery pipelines.

Azure DevOps (Defect Tracking & Traceability): Log, triage, and track defects and data issues, maintain traceability to User Stories, data changes, and releases, and work with Engineering and Data teams on root-cause analysis and fix validation.

SQL (Data Validation): Write and execute queries to validate data transformations, reconciliations, referential integrity, and data quality rules across staging and curated layers.

Postman & Swagger (API Testing): Validate API contracts, schemas, authentication and authorization, error handling, and payload accuracy using Swagger/OpenAPI specifications; create and maintain collections for smoke and regression testing.

Databricks & Azure Data Factory: Support validation of transformation logic, pipeline outcomes, and orchestration processes. Databricks is the primary platform used in DPP, while Azure Data Factory knowledge remains valuable as an additional skill aligned to broader ETL requirements.

Programming & Frameworks:

Python or Java: Experience using one or both languages for test automation, API checks, and data validation scripting.

PyTest / Robot Framework / Contract Testing / Automated Data Checks: Familiarity with test frameworks and data-quality tooling to support DPP regression coverage and ongoing monitoring.

Test Execution Delivery: Complete planned DPP test execution within agreed sprint timelines, with clear evidence and status visibility.

Defect Detection Rate: Identify and log at least 90% of critical defects (data quality issues, pipeline failures, and API contract breaks) before release.

Defect Turnaround Time: All defects are triaged, prioritized, and assigned within 24 hours of discovery.

Coverage & Traceability: Maintain end-to-end coverage of User Stories, acceptance criteria, core business rules, and key data scenarios, with clear traceability across testing and release activity.

Data Quality & Integrity: Ensure validated datasets meet transformation rules, reconciliation checks, and agreed quality tolerances for the DPP platform.

Status Reporting: Provide weekly QA status updates covering test progress, defect trends, data quality outcomes, pipeline/API risks, and release readiness.

Continuous Improvement: Propose at least one process or tooling improvement per quarter (e.g., automation, data-quality checks, or pipeline monitoring) to strengthen DPP quality.