OS
$223,000 – $239,000
Listed on Obsidian Securty’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
A Staff Software Engineer role leading cross-team platform and product work at a SaaS security company protecting enterprise applications. This suits experienced engineers who thrive in ambiguity, design systems at scale, and can influence technical direction across multiple teams.
Our summary, not Obsidian Securty’s wording. The full posting is on their site.
Skills this role names
- Amazon Web Services (AWS)
- Apache Kafka
- Docker
- GitLab CI/CD
- Go
- Grafana
- Kubernetes
- LLMs (Large Language Models)
- Prometheus
- Python
- Redis
- SQL
- TypeScript
- Vector Databases
Log in to see which of these are already on your profile.
What they ask for
Required
- 8+ years software engineering experience with increasing scope and impact
- Proficiency in Python, Go, TypeScript, or SQL
- Experience designing and building backend services, APIs, distributed systems, or data processing workflows at scale
- Deep understanding of relational databases like Postgres
- Experience with AWS or GCP cloud platforms
- Familiarity with Docker and Kubernetes
- Experience with event-driven or streaming systems like Kafka or Redis
- Ability to reason about system design across performance, reliability, security, and operational tradeoffs
- Experience setting technical direction and leading cross-team initiatives
- Strong communication in code reviews, design discussions, and cross-functional planning
Nice to have
- Observability and monitoring tools like Grafana or Prometheus
- CI/CD pipelines and deployment tooling such as GitLab CI/CD
- Large-scale distributed systems or high-throughput data pipeline architecture
- Background or interest in security, SaaS platforms, identity, threat detection, or data protection
- Experience across multiple or geographically distributed teams
- Mentoring engineers and improving technical practices
- Experience evaluating and using AI-generated code
- Familiarity with LLMs, embeddings, vector databases, and inference