Senior Software Engineer - Data Pipelines
Founded in 2017, Obsidian Security was created to close a critical gap: securing the SaaS applications where modern business happens—platforms like Microsoft 365, Salesforce, and hundreds more.
Backed by top investors including Greylock, Norwest Venture Partners, and IVP, we’ve built a complete SaaS security platform to reduce risk, detect and respond to threats, and prevent breaches at the source. Our team includes leaders who helped define the categories of endpoint and identity security at CrowdStrike, Okta, Cylance, and Carbon Black.
Now, we’re transforming how SaaS is secured—in the era of agentic AI.
Today, Obsidian is trusted by global enterprises like Snowflake, T-Mobile, and Pure Storage. We protect more than 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.
With strong global momentum, a growing partner ecosystem including SentinelOne, Databricks, and Google Cloud, and a major fundraise on the horizon, we’re scaling quickly toward long-term growth and IPO readiness. Join us as we define the future of SaaS security!
About the Role - as a Senior Engineer at Obsidian, you’ll:
- Build or extend data pipelines that power Obsidian’s Agentic AI and supply chain risk products.
- 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.
- Identify and proliferate best practices in data pipeline design between both the UK and US teams.
What’s in It for You
- You will 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
- Experience in influencing design patterns based on best practices.
- Experience assessing alternative data architecture designs to produce accurate and unbiased recommendations.
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
- Experience in forming strong stakeholder relationships to help promote joint working between teams.
- Experience of working alongside both UK and US development teams.
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.
