Senior Backend Engineer

Obsidian Security is the leading SaaS security platform, trusted by global enterprises like Snowflake, T-Mobile, and Algolia. We protect 200+ organizations across North America, Europe, the Middle East, Southeast Asia, Australia, and New Zealand, including many of the world’s largest Fortune 1000 and Global 2000 companies. Founded in 2017 and backed by top investors like Greylock, Obsidian was built to close a critical gap: securing SaaS apps where business happens—Microsoft 365, Salesforce, and hundreds more. The company does this by offering a complete SaaS security platform to reduce risk, detect and respond to threats, and prevent breaches at the source. Obsidian was built by leaders who redefined endpoint and identity security at CrowdStrike, Okta, Cylance, and Carbon Black. Now, they’re transforming how SaaS is secured. With AI driving rapid SaaS growth and complexity, agentic AI tools gain privileged access to sensitive data through integrations, creating new risks most security tools miss. Obsidian uniquely detects anomalous OAuth token activity and manages integration risks. Major announcements are on the horizon. Recognizing that SaaS security needs to evolve, Obsidian enables growing organizations to start with a lightweight, prevention-focused browser extension and expand coverage over time. With global momentum, a growing partner ecosystem including SentinelOne, Databricks, and Google Cloud, and a major fundraise ahead, Obsidian is scaling rapidly toward long-term growth and IPO readiness. About the Role - as a Senior Backend Engineer at Obsidian, you’ll: Build new backend processing systems that power Obsidian’s core product Maintain, improve, and evolve existing systems to ensure performance, resilience, and scalability Design and implement APIs and backend services, including multithreaded applications Collaborate with product and engineering teams to support key product themes and ensure delivery of high-impact features Apply strong software engineering practices to requirements gathering, system design, and code reviews Contribute to a fast-moving, collaborative environment where adaptability and teamwork are essential What’s in It for You Have direct impact on the core product used by enterprises worldwide Work alongside a talented, friendly team in a supportive and collaborative culture Grow your skills with opportunities to learn new technologies and engineering practices Be part of an innovative, fast-paced environment where your contributions are valued Enjoy a hybrid working, with supported remote working and great office spaces in Cheltenham and Manchester Required Skills & Experience 5-7 years of experience in a software engineering role Proficiency in one or more modern programming languages such as Python, Go or SQL Experience building backend services, APIs, and multithreaded applications Familiarity with containerization and orchestration technologies such as Docker and Kubernetes Strong knowledge of relational databases (e.g., Postgres) Experience collaborating in team environments and adapting to changing requirements Understanding of software design principles and engineering best practices Experience with cloud platforms (AWS, GCP), object storage (S3), or event/streaming systems (Kafka, Redis) Familiarity with Git for version control and deployment tooling such as GitLab CI/CD Desirable Experience Experience with system monitoring and observability tools such as Grafana, Prometheus, or similar platforms Understanding of quality engineering (QE) practices across development and testing lifecycles Exposure to large-scale distributed systems and performance optimisation AI Skills & AI-Native Engineering Expectations: As an AI-forward engineering organization, we expect senior engineers to effectively leverage AI tools and understand foundational AI concepts to enhance development efficiency and build AI-ready systems. AI Engineering Capabilities: Leverage AI tools effectively to improve development efficiency and build AI-ready systems. Proficient with AI-powered developer tools; able to critically evaluate and refine AI-generated outputs. Strong understanding of core AI/ML concepts (LLMs, embeddings, vector databases, inference, evaluation). Experience integrating AI/ML APIs and building AI-ready data infrastructure (e.g., for RAG). Ensure data quality, governance, observability, reliability, security, and performance in AI-driven systems.