Staff Software 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 worlds 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 happensMicrosoft 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 theyre 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 Staff Software Engineer at Obsidian youll:

Own meaningful product and platform work across Obsidians engineering stack with scope that extends beyond a single team. This is a generalist role for someone who can move between backend services data flows APIs product features integrations and production systems without getting stuck at the boundaries between them and who can set technical direction that other engineers and teams align to.

Youll work on systems that help customers understand activity risk and threats across their SaaS environments. Some problems will be well-defined. Others will start as a customer need a security signal or a vague product idea that needs technical shape. You should be comfortable turning that ambiguity into an architecture a multi-team plan and working software and comfortable being the person others look to when the technical path forward is unclear.

What youll do

• Own features and system architecture from problem definition through production at a scope spanning multiple teams or the broader platform
• Design and build backend services APIs data processing workflows integrations and product-facing capabilities
• Set technical direction and architectural standards that other engineers and teams build against
• Work with product managers designers security researchers and engineers to turn customer needs into shipped software
• Improve the performance reliability scalability and observability of existing systems and identify systemic risks before they become incidents
• Make practical technical decisions with organization-wide impact and explain the tradeoffs clearly to both technical and non-technical stakeholders
• Raise engineering standards through code reviews design discussions mentoring senior and mid-level engineers and documentation
• Debug the hardest production issues and drive teams to durable fixes not just short-term patches
• Use AI-powered tools effectively while maintaining high standards for correctness security and maintainability

Whats in It for You

• Direct high-leverage impact on a core security product used by enterprises worldwide
• Variety across backend engineering data pipelines SaaS integrations detection systems cloud infrastructure and customer-facing workflows
• A chance to define the technical approach to current security problems around SaaS identity data movement and AI usage
• Strong peers across product engineering security research and go-to-market teams
• A fast-moving environment where staff/principal engineers are expected to own ambiguous problems influence direction across teams and ship

Required Skills & Experience

• 8 years of experience in a software engineering role with a track record of increasing technical scope and impact
• Strong proficiency in one or more modern programming languages such as Python Go TypeScript or SQL
• Demonstrated experience designing and building backend services APIs distributed systems data processing workflows or production product features at scale
• Deep understanding of relational databases such as Postgres including performance and scaling tradeoffs
• Experience with cloud platforms such as AWS or GCP including cost reliability and architecture tradeoffs
• Familiarity with containerization and orchestration technologies such as Docker and Kubernetes
• Experience with event-driven or streaming systems such as Kafka Redis or similar technologies
• Strong ability to reason about system design performance reliability security and operational tradeoffs across multiple systems and teams
• Experience setting technical direction and leading complex cross-team technical initiatives without needing formal authority
• Strong communication skills in code reviews design discussions and cross-functional planning including with senior stakeholders

Desirable Experience

• Experience with observability and monitoring tools such as Grafana Prometheus or similar platforms
• Experience with CI/CD pipelines and deployment tooling such as GitLab CI/CD
• Deep exposure to large-scale distributed systems high-throughput ingestion or data pipeline architecture
• Background or interest in security SaaS platforms identity threat detection or data protection
• Experience working across multiple engineering teams or across geographically distributed teams
• Experience mentoring engineers including senior engineers and helping teams improve technical practices
• Experience evaluating AI-generated code and using AI tools as part of an engineering workflow

AI Skills & AI-Native Engineering Expectations

As an AI-forward engineering organization we expect staff/principal engineers to effectively leverage AI tools and understand foundational AI concepts to improve development efficiency build AI-ready systems and set the standard for how their teams use AI tools responsibly.

AI Engineering Capabilities

• Leverage AI tools effectively to improve development efficiency and build AI-ready systems
• Critically evaluate test and refine AI-generated outputs before they reach production
• Understand core AI/ML concepts such as LLMs embeddings vector databases inference and evaluation
• Experience integrating AI/ML APIs or building systems that support AI-driven product workflows
• Ensure quality observability reliability security and performance in systems that use or support AI capabilities
• Help establish org-wide norms and best practices for AI-assisted engineering