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 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 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, you'll:

Own meaningful product and platform work across Obsidian's 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.

You'll 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 you'll 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

What's 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