Senior Security Data 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 We're hiring Security Data Engineers to join our Manchester team and work at the heart of our SaaS and AI security platform. This is a data engineering role first — you'll spend most of your time building and maintaining the data pipelines that power Obsidian's detection and analytics capabilities. The role sits at an interesting intersection: the work is technically demanding data engineering, but the context is security. You don't need a security CV — you need to be a strong data engineer who is curious about how modern SaaS and AI platforms work, and what makes them risky. What you'll do Build and maintain data retrieval integrations across major SaaS and AI platforms — connecting to APIs, handling auth flows, and ensuring reliable telemetry ingestion. Design and develop dbt models in SQL on Clickhouse and Databricks, transforming raw platform data into clean, structured datasets ready for analysis. Build rule-based checks to identify risky configurations and unusual activity patterns across SaaS and AI platforms — translating security insight into logic. Develop a working understanding of how SaaS and AI platforms are structured and where security risks emerge, to inform what data we collect and what we look for. Debug data quality and detection issues in customer environments, working closely with the wider engineering and customer success teams.= What we're looking for Requirements 4+ years of hands-on data engineering experience, including dbt and SQL. Strong SQL — you're comfortable writing complex data models and transformations from scratch. Experience building or maintaining data pipelines that process high-volume event or log data. Comfortable working with REST APIs and integrating data from third-party platforms. Python proficiency for scripting, data manipulation, and pipeline logic. Some exposure to security concepts — enough to understand what 'risky' looks like in a SaaS or cloud context. This can come from working in a security company, cloud engineering, compliance, or your own interest. Nice to have Hands-on experience with Clickhouse or Databricks. Familiarity with SaaS platforms such as Google Workspace, Microsoft 365, Salesforce, or Okta — from any angle (admin, engineering, or security). Understanding of identity and authentication concepts: OAuth, SAML, SSO, and directory services. Experience working with security telemetry — audit logs, API event streams, or similar. Interest in AI/LLM pl