Senior Quality Analyst

Volantsoft Inc•Dallas, TX

About The Position

We are seeking an experienced Senior Quality Analyst to support the PISCES HUB initiative. The ideal candidate will have strong experience testing cloud-native, configuration-driven, event-based, and data-processing platforms. This role requires hands-on expertise in Python-based test automation, AWS services, Kafka, data pipeline validation, API testing, database testing, CI/CD, containerization, observability, and production-quality engineering.

Requirements

7+ years of overall experience in software quality engineering or software engineering.
5+ years of hands-on experience testing AWS-based applications, Kafka integrations, and data pipelines.
Demonstrated experience designing and implementing automated testing frameworks using Python.
Strong proficiency in Python, including the development of reusable automation frameworks and test utilities.
Hands-on experience with Python testing tools such as Pytest, unittest, requests, or equivalent frameworks.
Strong experience testing AWS data pipelines and cloud-native applications.
Hands-on knowledge of the following AWS services: Amazon EKS, AWS Lambda, Amazon ECS, Amazon S3, Amazon RDS, Amazon DynamoDB
Strong experience testing Apache Kafka-based event-driven architectures.
Proven experience validating: Metadata-driven pipelines and workflows, Dynamic business-rule engines, Configuration-driven applications, Configurable APIs and data-integration frameworks
Experience testing JSON-, YAML-, and XML-based configuration interpreters.
Strong SQL skills and experience validating relational and NoSQL data stores.
Understanding of database models used to store, version, audit, and retrieve rule configurations.
Experience with API testing tools and frameworks such as Postman, REST Assured, or Python Requests.
Strong understanding of CI/CD pipelines, Git, and GitOps practices.
Hands-on experience with Docker, Kubernetes, and containerized application testing.
Strong working knowledge of Dynatrace for monitoring, observability, performance analysis, and troubleshooting.
Working knowledge of Windsurf or similar AI-assisted development tools.
Strong analytical and problem-solving abilities.
Quality-first mindset with strong attention to detail.
Ability to understand complex data flows and distributed system architectures.
Strong debugging and root-cause-analysis skills.
Ability to work independently and collaborate across engineering teams.
Clear written and verbal communication skills.
Ability to identify quality risks and communicate release-readiness recommendations to stakeholders.

Nice To Haves

Experience testing high-volume, distributed, event-driven platforms.
Experience with data reconciliation, schema evolution, data-contract testing, and data-quality validation.
Familiarity with Kafka schema management and serialization formats such as Avro, JSON, or Protobuf.
Experience with performance-testing tools such as JMeter, Locust, or Gatling.
Familiarity with infrastructure-as-code and deployment tools such as Terraform, Helm, or Argo CD.
AWS or software-testing certifications are an advantage.

Responsibilities

Develop and execute comprehensive test strategies for the PISCES HUB platform.
Design and maintain Python-based automation frameworks for API, integration, data pipeline, and end-to-end testing.
Validate AWS-based data pipelines, event-driven workflows, and distributed application components.
Test Kafka producers, consumers, topics, partitions, schemas, offsets, retries, and failure-recovery scenarios.
Validate data accuracy, completeness, consistency, transformation logic, lineage, and reconciliation across pipelines.
Test configuration-driven solutions, including metadata-driven workflows, dynamic business rules, and configurable integration frameworks.
Validate JSON-, YAML-, and XML-based configurations, including schema validation, version compatibility, error handling, and backward compatibility.
Perform functional, integration, regression, system, and negative testing.
Validate REST APIs, asynchronous services, batch processes, and event-based integrations.
Test SQL and NoSQL databases for data integrity, rule-configuration storage, versioning, auditability, and rollback capabilities.
Integrate automated test suites with CI/CD pipelines and GitOps workflows.
Test containerized workloads deployed using Docker, Kubernetes, Amazon EKS, and Amazon ECS.
Use Dynatrace to monitor application behavior, analyze logs and traces, identify performance bottlenecks, and support root-cause analysis.
Collaborate with developers, architects, product owners, DevOps engineers, and data engineers to define acceptance criteria and improve overall product quality.
Track defects through resolution and provide clear reporting on testing progress, quality risks, and release readiness.
Use Windsurf and other AI-assisted engineering tools to accelerate test-case creation, automation development, troubleshooting, and documentation.