QA Engineer

• - Help us keep the quality bar high across the board.
• - Work on features like identity verification via photo ID, virtual credit cards for hotel payments, and two-factor authentication.

Quality Assurance Engineer, Manual

• Defining the automated testing strategy for the project
• Designing and maintaining test cases for new and existing functionalities
• Creating automated tests for Java Spring Boot services, REST APIs, integrations, and Camunda workflows
• Testing BPMN processes, DMN rules, process variables, service tasks, error handling, retries, timers, and external integrations
• Building and maintaining automated regression test suites
• Integrating automated tests into the CI/CD pipeline
• Working with developers to improve testability, code quality, and defect prevention
• Reviewing requirements, user stories, acceptance criteria, BPMN diagrams, and technical designs to identify test scenarios
• Managing test data, mocks, stubs, and test environments
• Reporting, tracking, and helping resolve defects
• Promoting good testing practices within the development team

SSR QA Automation

• Asegurar la calidad funcional de las aplicaciones mediante pruebas manuales efectivas.
• Comprender rápidamente distintos contextos de negocio y detectar riesgos antes de salir a producción.
• Participar activamente en el ciclo de testing.
• Colaborar con desarrollo y proponer mejoras continuas en procesos.
• Foco en evolucionar hacia automatización e incorporación de herramientas de IA.

• Validate data accuracy and ensure production readiness by working closely with the Data Engineering Manager and Analytics Engineering Lead across 18 data domains.
• Design and build a reconciliation framework to systematically compare legacy pipeline outputs against new pipeline outputs, identifying discrepancies and gaps.
• Execute structured acceptance testing for each pipeline prior to promotion to production environments.
• Validate identity resolution accuracy through rigorous analysis of match rates, false positives, and false negatives.
• Document end-to-end data lineage across all 18 domains to support auditability, transparency, and regulatory compliance.
• Build and maintain automated regression test suites to enable continuous quality assurance as pipelines evolve.

Data QA Analyst

• Lead and evolve the QA strategy across products, teams, and markets.
• Own end-to-end product quality across Web, Mobile, and backend systems.
• Coordinate QA efforts across squads, ensuring high-quality releases and scalable processes.
• Drive automation initiatives and integrate testing into CI/CD pipelines.
• Collaborate closely with Product, Engineering, and UX teams to embed quality early in the development lifecycle.
• Monitor quality KPIs and continuously improve processes, workflows, and delivery standards.
• Mentor and support QA team members across manual and automation testing practices.

QA Coordinator

• Execute Functional QA: Perform meticulous manual visual and functional QA to ensure products meet design mocks and business requirements.
• Develop Automation: Build and maintain automated test suites using frameworks like Cypress to support continuous integration and delivery.
• Manage Bug Lifecycle: Lead regression testing efforts and manage the end-to-end bug-tracking lifecycle, from discovery to resolution.
• Test CMS Environments: Validate configurations and content delivery within Brightspot and other enterprise CMS platforms.
• Own Quality Strategy: Define and execute test plans that translate complex business needs into rigorous quality assurance blueprints.
• Collaborate Globally: Work closely with US-based product owners and European delivery teams, requiring a proactive communication style and flexibility for early-morning syncs.
• Ensure Quality Standards: Champion best practices in security testing, accessibility auditing, and performance benchmarking for enterprise deployments.

Senior QA Engineer

• Ensure data quality, reliability, and consistency across modern data platforms.
• Validate data pipelines and implement automated quality checks.
• Collaborate closely with Data Engineering and business teams.
• Design and implement a data quality framework across Bronze, Silver, and Gold layers.
• Build and maintain automated data quality checks within Databricks pipelines.
• Own reconciliation between source systems and Databricks layers.
• Validate identity resolution outputs in the Silver layer.
• Perform end-to-end pipeline testing to ensure accurate data flows.
• Partner with Data Engineers to define acceptance criteria.
• Support UAT with client business stakeholders.
• Document all QA processes and findings.
• Monitor pipeline health post-deployment and resolve data quality incidents.

Data Quality Engineer

• Own, maintain, and evolve our test automation framework (Java, RestAssured, JUnit/TestNG) to support backend services and APIs at scale.
• Define and continuously improve the multi-layer testing strategy (unit, component/contract, integration, E2E), coverage, and risk-based prioritization.
• Partner with cross-functional squads to embed quality by design, enabling fast feedback and reliable deployments.
• Set, document, and enforce standards for test design, reliability, flakiness control, performance, and maintainability.
• Review test designs and automation code (PRs/code reviews); ensure clean architecture and reusable patterns.
• Mentor and coach QA and software engineers on testing best practices; lead workshops and pair sessions.
• Lead the approach for non-functional testing (performance and security), coordinating with SRE/Sec stakeholders.
• Improve CI/CD pipelines for test efficiency (parallelization, selective test runs, test data mgmt., quality gates).
• Identify and deliver automation opportunities (e.g., contract testing, mocks/service virtualization, reusable test data, observability hooks) to remove manual checks and reduce errors.
• Track and communicate quality metrics (stability, performance signals) and use telemetry to spot gaps proactively.

QA Automation – Technical Referent

• Help shape a QA organization recognized for its high standards of quality, efficiency, and predictability - by blending your deep human expertise with AI-augmented and agentic testing practices.
• Leverage generative AI, agentic testing, and self-healing automation to accelerate test creation, maintenance, and coverage.
• Research, pilot, and implement AI-powered QA tools and methodologies to improve quality and reduce manual effort.
• Collaborate with engineering and product teams to integrate AI-driven quality gates into delivery pipelines, positioning QA as a proactive strategic partner.
• Develop and implement test plans, test cases, and test scripts for both automated and manual testing.
• Design, build, and maintain automated test frameworks to streamline testing processes and improve efficiency.
• Execute manual tests to identify bugs, defects, and usability issues, ensuring comprehensive test coverage.
• Collaborate closely with development teams to understand project requirements and verify software functionality.
• Perform regression testing, load testing, and performance testing to assess product quality and performance.
• Analyze test results, troubleshoot issues, and communicate findings to relevant stakeholders.
• Participate in Agile development processes, including sprint planning, daily stand-ups, and retrospectives.
• Continuously evaluate and enhance QA processes, tools, and methodologies to optimize testing efforts.
• Manage QA workstreams across multiple simultaneous client projects, coordinating timelines and priorities with project managers.

Senior QA Engineer – AI, Test Automation

• Manage exploratory and automated tests to ensure product quality.
• Design and implement tests, debug and define corrective actions.
• Review system requirements and track quality assurance metrics (e.g., defect densities and open defect counts).
• Monitor all stages of software development to identify and resolve system malfunctions.
• Prepare detailed, comprehensive, and well-structured test plans and test cases.
• Review requirements, specifications, and technical design documents to provide feedback.
• Plan and coordinate testing activities, thoroughly identify and document bugs.
• Build and execute automation scripts using open source tools.
• Organize testing processes for new and existing products to meet client needs.
• Collaborate with internal teams to identify system requirements.
• Track quality assurance metrics, like defect densities and open defect counts.
• Analyze the causes of non-conforming software and train users to implement solutions.
• Maintain relevant knowledge about new testing tools and test strategies.
• Explore opportunities where Artificial Intelligence can enhance product capabilities or internal processes.

QA Engineer – Javascript, Typescript

• Ensure data quality, reliability, and consistency across modern data platforms.
• Validate data pipelines and implement automated quality checks.
• Collaborate closely with Data Engineering and business teams to guarantee accurate and production-ready data assets.
• Design and implement a data quality framework across Bronze, Silver, and Gold layers.
• Build and maintain automated data quality checks within Databricks pipelines.
• Own reconciliation between source systems and Databricks layers.
• Validate identity resolution outputs in the Silver layer.
• Perform end-to-end pipeline testing.
• Partner with Data Engineers to define acceptance criteria for each sprint’s pipeline and data model deliverables.
• Support UAT with client business stakeholders.
• Document all QA processes, test results, and data quality findings.
• Monitor pipeline health post-deployment.

QA Analyst, Databricks

• Serás responsable de liderar la migración de la aplicación nativa Android hacia React Native siguiendo una estrategia brownfield/strangler
• Implementar capacidades offline-first y sincronización de datos
• Asegurar coherencia estética y funcional con la aplicación web
• Tomar decisiones técnicas con criterio
• Ejecutar con solidez y colaborar estrechamente con otros referentes del equipo para garantizar una experiencia móvil robusta y de alto impacto.

Senior QA Automation

• Ejecutar pruebas funcionales sobre aplicaciones web y mobile.
• Analizar requerimientos y casos de uso para diseñar y ejecutar escenarios de prueba.
• Detectar, registrar y dar seguimiento a defectos, comunicando claramente el estado de las pruebas.
• Trabajar bajo restricciones de tiempo, priorizando correctamente los casos a ejecutar.
• Colaborar con el equipo en un contexto de alta carga operativa.
• Adaptarse a nuevos procesos y normativas, propios del negocio de inversiones.
• Participar activamente de las ceremonias del equipo y proponer mejoras al proceso de testing.

Junior QA

• Conduct audits of AI-generated outputs across large-scale deployments to ensure data accuracy, formatting consistency, and logical flow.
• Monitor active AI agents and automation workflows; identify, diagnose, and resolve technical bottlenecks or API-related failures.
• Collaborate with internal teams and clients to identify operational gaps and implement technical fixes that enhance tool reliability and user experience.
• Utilize platforms such as Cursor and Claude to refine existing code, troubleshoot logic, and implement technical fixes that enhance tool reliability.

QA Optimization, AI Developer

• You will build and own the QA function at Peach Pilot — writing test code, designing eval pipelines, and setting the quality bar as we move from early-stage development into full production and enterprise deployment.
• This is a fully remote contract role based in Latin America.
• You will work directly with the US-based founding engineering team and must be available during US Eastern business hours with a minimum of 5 hours of daily overlap.
• Establish the testing framework from zero: unit, integration, end-to-end, and LLM-specific evaluation pipelines
• Define quality standards, test coverage requirements, and documentation practices in partnership with the VP of Engineering
• Audit the existing platform and identify the highest-risk surfaces before the next major customer deployment
• Validate the Enterprise Knowledge Graph for data accuracy, retrieval quality, and failure modes under real enterprise data conditions

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