Principal Engineer, Data Platform - QA

Kindness and a human-first, empathetic approach to teamwork and communication. - 10+ years of experience in Quality Assurance, with at least 7 years focused on automation, with Computer Science or equivalent practical experience. - Strong background in testing data-intensive or observability systems. - Proficiency in Python, Go, or Java for automation and tooling. - Experience with automation frameworks and performance testing tools. - Expertise in streaming data validation, event-driven architectures, and warehouse/data lake performance tuning. - Familiarity with cloud-native architectures, CI/CD pipelines, and DevOps practices. - Excellent analytical, debugging, and communication skills. Role Overview: At F5, as a Principal QA Engineer, you will play a critical role in leading the quality strategy for the next-generation data infrastructure that supports mission-critical workloads across the SaaS platform. Your responsibilities will include designing test frameworks, defining data validation strategies, leading performance benchmarking, and mentoring team members on reliability, automation, and best practices for large-scale distributed data systems. Key Responsibilities:

Design QA strategies for data streaming, storage, and observability services. - Collaborate with Engineering, Product, SRE, and Platform teams to embed quality and reliability throughout the SDLC. - Develop an automation framework for data validation, regression, and integration testing. - Extend automation capabilities to handle real-time data streams, schema evolution, workflows, and data consistency checks. - Design and execute tests for streaming platforms (e.g. Kafka), ETL pipelines, and data storage systems (ClickHouse, ElasticSearch, Iceberg, S3). - Create tools to validate ingestion, transformation, and query accuracy. - Automate validation of logs, metrics, and traces for correctness & completeness. - Validate telemetry and SaaS usage pipelines (e.g. Prometheus, OpenTelemetry). - Simulate failure and recovery scenarios for distributed systems. - Ensure system instrumentation for high coverage automated observability testing. - Validate deployments across multi-cloud and K8s native data clusters. - Implement chaos and resilience testing for data system components. - Collaborate with SRE/Ops