Job highlights

- Experience testing Java applications,REST APIs,microservices,batch processing,and distributed systems
- Practical experience validating solutions on Google Cloud Platform (GCP),including Cloud Storage,BigQuery,Pub / Sub,Dataflow,or Cloud Monitoring
- Experience with Apache Beam,batch or streaming data pipelines,schema evolution,watermarking,and data-quality controls

Short summary of JD: Senior QA Engineer - GCP | Deloitte USI (DLTJP00062622)

- Experience: QA testing for Java, APIs, microservices, and data ingestion frameworks.
- Mandatory Skills: GCP, SQL, BigQuery, REST API Testing, Automation Testing, Kubernetes.
- Key Expertise: Full/Incremental Loads, Data Validation, Source-to-Target Reconciliation, Regression Testing, CI/CD.
- Additional Skills: Cloud Monitoring, Pub/Sub, Dataflow, Performance Resilience Testing.
- Responsibilities: Test strategy, test case design, defect management, automation, and release validation across SIT, PRE-PROD, and PROD

USI-CTH Senior QA Engineer (GCP)

Job Posting ID

DLTJP00062622

Role Summary

We are seeking a hands-on QA Engineer or QA Analyst to validate the GCP ingestion framework developed with Java, microservices, Google Cloud Platform (GCP), and Kubernetes. The successful candidate will define and execute a comprehensive quality strategy for enterprise data ingestion and downstream processing.

This role will analyze the frameworks built, create traceable test coverage, and validate full loads, incremental loads, and partition-overwrite loads. The candidate will test framework reliability, data accuracy, error handling, alerting, and operational readiness, then provide evidence for signoff and support promotion through SIT, PRE-PROD, and PROD environments.

Key Responsibilities

Test strategy and requirements traceability

- - Review functional, non-functional, interface, data-quality, security, and operational requirements for testability.
- Create a risk-based test strategy, test plan, test scenarios, and traceability matrix for each framework capability.
- Define entry criteria, exit criteria, test data needs, environment dependencies, and signoff evidence.

Maintain traceability from frozen requirements through test cases, defects, retests, and release approval.

- - Functional and data validation
- Validate full-load processing, including file or message discovery, ingestion, transformation, and target loading.
- Validate incremental-load processing, including watermarking, changed-data detection, restartability, and duplicate prevention.
- Validate partition-overwrite processing, including partition selection, replacement behavior, and protection of unaffected data.




- Reconcile source and target record counts, control totals, schemas, data types, business keys, and representative field values.

Test file, API, database, messaging, and cloud-storage ingestion scenarios as applicable to each downstream pattern.

- - Verify schema evolution, malformed records, null values, duplicate records, empty inputs, large files, and boundary conditions.
- Automation and test tooling
- Develop and maintain automated API, integration, data-validation, regression, and smoke tests for the framework.
- Automate repeatable execution across configuration variations, ingestion patterns, and representative data volumes.
- Use Java-based test frameworks and suitable tools for REST services, databases, files, messaging, Big Query, and GCP workflows.

Integrate automated tests into CI/CD quality gates and publish clear test results and evidence.

- - Manage reusable test data, mocks, stubs, service virtualization, and environment-independent test setup.
- Track automation coverage, flaky tests, execution trends, and gaps requiring manual validation.
- Resilience, observability, and non-functional testing
- Verify retries, timeouts, idempotency, graceful failure, dead-letter or error-sink handling, and reprocessing behavior.
- Validate Cloud Monitoring metrics, logs, dashboards, alert policies, notification routing, and SLA-breach detection.
- Perform volume, performance, concurrency, recovery, and soak testing for expected and peak ingestion loads.
- Validate Kubernetes deployment health, readiness and liveness behavior, configuration, scaling, and restart scenarios.
- Assess security controls including authentication, authorization, secrets handling, data access, and least-privilege behavior.
- Defect management and release readiness
- Log explicit, reproducible defects with business impact, severity, evidence, logs, test data, and suspected failure points.
- Coordinate triage with developers, platform engineers, data owners, and downstream teams, and verify fixes through targeted retesting.

Manage regression cycles, defect aging, risk acceptance, known issues, and release-readiness reporting.

- - Prepare test completion reports, quality metrics, traceability evidence, and recommendations for formal signoff.




- Support deployment validation and production smoke testing across SIT, PRE-PROD, and PROD environments.
- Required Qualifications
- Hands-on experience in software quality assurance for enterprise applications, data platforms, APIs, or ingestion frameworks.

Strong experience creating test strategies, test plans, test cases, traceability matrices, and release-quality reports.

- - Experience testing Java applications, REST APIs, microservices, batch processing, and distributed systems.
- Practical experience validating solutions on Google Cloud Platform (GCP), including Cloud Storage, BigQuery, Pub/Sub, Dataflow, or Cloud Monitoring.
- Experience testing full-load, incremental-load, partition-overwrite, watermarking, reconciliation, and idempotency scenarios.
- Strong SQL and data-validation skills, including source-to-target reconciliation, schema checks, and control-total validation.
- Experience with automated testing, CI/CD integration, defect management, regression testing, and test reporting.
- Experience validating Kubernetes or containerized applications, service health, configuration, and deployment behavior.
- Ability to test error handling, retry behavior, alerting, logging, security controls, performance, and recovery scenarios.
- Ability to analyze legacy behavior, clarify requirements, identify risks, and communicate quality impacts to stakeholders.
- Ability to support testing and release validation across SIT, PRE-PROD, and PROD environments.
- Preferred Qualifications

Experience with ATLAS or comparable enterprise ingestion frameworks and legacy-framework modernization.

- - Experience with Docker, Kubernetes, Helm, Terraform, CI/CD pipelines, and cloud-native test execution.
- Experience with Apache Beam, batch or streaming data pipelines, schema evolution, watermarking, and data-quality controls.
- Experience creating dashboards, quality metrics, alert validation, and incident-response evidence for data platforms.
- Experience with performance testing, large-volume data loads, recovery testing, and production-readiness reviews.
- Experience working in regulated, complex, or large-scale enterprise environments.
- Experience with Agile delivery, test estimation, backlog refinement, and collaboration across distributed teams.
- Knowledge of cloud security, IAM, secrets management, network boundaries, and least-privilege validation.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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