Principal Software Engineer, Distributed Cloud

This is a hands-on, technical role where you will design test frameworks, define data validation strategies, lead performance benchmarking, and mentor team on reliability, automation, and test best practices for large-scale distributed data systems throughout the development lifecycle.

Key Responsibilities

Quality Strategy & Automation Framework

• DesignQA strategiesfor data streaming, storage, and observability services.

• Partner with Engineering, Product, SRE, and Platform teams to embed quality and reliability throughout the SDLC.

• Build automation framework for data validation, regression, and integration testing.

• Extend automation to handle real-time data streams, schema evolution, workflows, and data consistency checks.

Data Systems & Observability Testing

• Design and execute tests for streaming platforms (e.g. Kafka), ETL pipelines, and data storage systems (ClickHouse, ElasticSearch, Iceberg, S3).

• Develop 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.

Cloud Infrastructure Quality & Performance

• Validate deployments across multi-cloud and K8snative data clusters.

• Implement chaos and resilience testing for data system components.

• Collaborate with SRE/Ops to ensure test environments are production-parity.

• Establish performance and load testing frameworks for streaming (e.g.Kafka topics/partitions), ingestion of APIs, and warehouse/Data lake(e.g.ClickHousequeries).

• Build synthetic data generators and benchmarking tools for large-scale test datasets.

• Analyze bottlenecks and help optimize system throughput and latency.

• Perform performance, scalability, and reliability testing to ensure our data platform can handle global-scale workloads.

QA Best Practice & Mentorship

• Integrate test frameworks into CI/CD pipelines, validate complex, distributed systems across multi-cloud environments.

• Identify, document, and track defects through resolution.

• Create and maintain test plans, test cases, and documentation.

• Participate in design and code reviews to ensure quality is built into every stage of development.

• Mentor junior QA engineers and promote best practices in test automation and quality assurance.

• Investigate production issues and contribute to root cause analysis and remediation strategies.

Required Skills & Experience

• 10+ years of experience in Quality Assurance, with at least 7 years focused on automation, with Computer Science or equivalent practical experience.

• Strong background testing data-intensive or observability systems (e.g.Kafka, Flink, Spark,ClickHouse,ElasticSearch, Prometheus,OpenTelemetry).

• Proficiencycoding/scripting skills in Python, Go, or Java for automation and tooling.

• Experience with automation frameworks (e.g.Seleniumor similar).

• Expertise in performance testing tools (e.g., Locust, Gatling, k6, JMeter) and benchmarking distributed systems.

• Expertise in streaming data validation, schema, and event-driven architectures.

• Exposure to warehouse/data lake performance tuning and query optimization.

• Familiarity with compliance validation in data pipelines (e.g. PII masking).

• Familiar with cloud-native architectures (K8s, Terraform, Helm, CI/CD pipelines).

• Experience testing in cloud, distributed systems, microservices, and APIs.

• Familiarity with CI/CD pipelines, version control (Git), and DevOps practices.

• Excellent analytical, debugging, and communication skills.

• Experience leading QA strategy in SaaS, observability, or analytics platforms.