Role Overview:
As a Senior SDET, you will bridge the gap between development, infrastructure, and quality assurance. You will be responsible for the resilience and scalability of our distributed graph database products (On-Prem and Cloud). This role is not just about finding bugs, it’s about building the automated infrastructure that prevents them, optimizing our K8s-based CI/CD pipelines, and leveraging AI to accelerate the testing lifecycle.
Roles & Responsibilities:
- Pipeline Orchestration: Own, extend, and optimize Jenkins CI/CD pipelines. Design new modules for automated deployment and testing within Kubernetes clusters and multi-cloud environments (AWS/Azure/GCP).
- Advanced Automation: Develop and maintain robust automation frameworks using Python (Pytest) and Selenium/Playwright for both backend engine validation and frontend cloud consoles.
- Resilience & Chaos Engineering: Design fault-injection scenarios to validate high availability (HA) and disaster recovery in distributed environments.
- Root Cause Analysis (RCA): Identify and reproduce complex non-deterministic issues across distributed nodes, including race conditions, distributed deadlocks, and network partitions.
- Infrastructure Bottlenecks: Isolate whether a failure is code-centric or infrastructure-related (e.g., EBS volume throttling on AWS, K8s OOM kills, or VPC peering latency).
- AI Integration: Identify opportunities to integrate AI/LLM capabilities (utilizing tools like Claude) to automate repetitive tasks, such as test data generation, log analysis, and flaky test identification.
- Graph-Specific Validation: Validate query languages (GSQL/Cypher) for syntax accuracy, execution performance, and scalability.
Requirements
Technical Requirements:
- Scripting & Development: Expert-level Python and Bash scripting. You should be comfortable writing sophisticated automation scripts, custom test hooks, and system-level utilities for distributed environments.
- Orchestration & Infrastructure: Strong experience with Jenkins (Groovy DSL), including the ability to build, extend, and maintain complex pipelines. Must be proficient with Docker and Kubernetes (specifically Helm charts, Operators, and K8s networking).
- Cloud Platforms: Hands-on experience with AWS, Azure, or GCP. This includes a deep understanding of cloud-native services, managed Kubernetes (EKS/AKS/GKE), and troubleshooting networking/storage layers.
- Testing Frameworks: Mastery of Pytest for backend and API validation, alongside professional experience with Selenium or Playwright for automating cloud-based GUI consoles.
- Performance & Scalability: Proficiency with distributed load testing tools such as Locust, K6, or JMeter to identify system bottlenecks and resource contention.
- AI Proficiency: Experience leveraging LLMs (like Claude or GPT) to accelerate the SDET lifecycle—specifically for refactoring code, automating documentation, generating synthetic test data, or analysing CI/CD logs.
Nice to have: "AI + SDET" Capabilities:
We are looking for an engineer who doesn't just work harder but works smarter.
- Prompt Engineering: Ability to use Claude/LLMs to refactor legacy test code or generate boilerplate scripts.
- Automated Diagnostics: Interest in building AI-assisted log parsers to reduce the time spent on manual "Root Cause Analysis."
- Predictive Testing: Ability to leverage AI to identify "at-risk" areas of the codebase to prioritize test execution.
