About Druva
Druva is the resilience foundation for the AI enterprise, helping organizations secure and recover from connected risk across data, cyber, identity, and AI. The Resilience Cloud is a fully managed, cloud-native SaaS platform that delivers air-gapped and immutable protection across cloud, SaaS, on-premises, endpoint, and edge environments. Powered by Dru MetaGraph, Druva’s graph-powered intelligence layer, the platform connects critical business context so customers can understand risk, respond faster, recover cleanly, and govern data with greater confidence.
Trusted by nearly 7,500 customers, including 75 of the Fortune 500, Druva helps safeguard the critical information and systems businesses depend on in an increasingly connected world.
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Team & Mission: Druva Product Platform
The Druva Product Platform team builds the shared core SaaS application foundation that every Druva product is built on. Our multi-tenant services, APIs, and control-plane capabilities manage tenant provisioning and lifecycle, Identity & Access Management (IAM/RBAC), distributed job scheduling, usage metering, and platform observability.
Because every Druva workload depends on this platform, quality and reliability are critical. A bug or latency spike in the platform propagates across the entire Druva ecosystem. Quality Engineering in this team is a pure software engineering discipline: we build scalable automation frameworks, and implement CI/CD quality gates that safeguard billions of operations each year.
Role Overview
As a Senior Software Engineer (QA / SDET) on the Druva Product Platform team, you will be the technical lead for quality engineering across distributed multi-tenant microservices, high-throughput APIs, and asynchronous event streams.
You will build test strategies, test automation frameworks in Python or Go, design functional test suites including scale & chaos engineering, partner with developers from day one (Shift-Left), and embed automated quality gates into CI/CD deployment pipelines.
Key Skills (Must-Haves):
- 5–8+ years of progressive experience in software quality engineering / SDET, with a focus on backend distributed systems and multi-tenant SaaS platforms.
- Strong coding skills in Python or Go (Golang) with a deep understanding of data structures, OOP principles, and test automation design patterns.
- Extensive experience in developing test automation frameworks from scratch (using frameworks like PyTest, Testify, RestAssured, Playwright, or Selenium).
- Advanced expertise in RESTful API and gRPC testing, including API contract validation, schema backward compatibility, and mock services.
- Hands-on experience with AWS Cloud services (EC2, S3, RDS, DynamoDB, Lambda, EKS) and containerization (Docker, Kubernetes).
- Strong proficiency in SQL for data validation, state verification, and synthetic test data generation.
- Demonstrated experience integrating automated test suites into CI/CD pipelines (GitHub Actions, Jenkins) with sub-15 minute execution loops and zero tolerance for flaky tests.
Desirable Skills (Nice-to-Haves):
- Experience with Chaos Engineering methodologies and tooling (Chaos Mesh, AWS FIS, LitmusChaos) to validate system fault tolerance.
- Domain expertise in data protection, backup/recovery, storage systems, virtualization, or cyber resilience.
- Familiarity with enterprise SaaS security testing (OWASP Top 10, auth flows, mTLS, RBAC testing).
- Experience testing event-driven architectures and streaming pipelines (Apache Kafka, AWS SQS/SNS).
- Experience testing Generative AI / LLM workflows and evaluating AI model outputs.
- Experience with Infrastructure as Code (Terraform) for provisioning ephemeral test environments.
Role and Responsibilities:
- Design, build, and maintain modular, scalable test automation frameworks in Python or Go for microservices, REST/gRPC APIs, and distributed event pipelines.
- Formulate comprehensive end-to-end test strategies and test plans for multi-tenant control plane capabilities, IAM, job orchestration, and metering.
- Drive "Shift-Left" quality by actively reviewing architectural RFCs and PRs, defining testability requirements, edge cases, and acceptance criteria upfront.
- Design, automate, and execute large-scale concurrency, and stress test suites to uncover bottlenecks before production releases.
- Implement Chaos Engineering experiments simulating node failures, network latency, and service degradation to validate system resilience.
- Embed automated regression, API, and integration suites into CI/CD pipelines, providing fast and reliable feedback gates to developers.
- Analyze production incidents, reproduce edge-case defects, and build permanent automated regression tests to prevent bug leakage.
- Track and improve Quality Engineering metrics: Defect Escape Rate, Automation Coverage (>90%), and Flaky Test Ratios (<1%).
- Mentor junior QA engineers and developers in automation framework architecture, clean testing patterns, and quality best practices.
Qualification:
- B.Tech / B.E / M.E / M.Tech in Computer Science, Information Technology, or equivalent practical experience