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As a Senior Software Engineer at Obsidian, you'll:

Own meaningful product and platform work across Obsidian's engineering stack. 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.

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 a design, a plan, and working software.

• Own features and system improvements from problem definition through production
• Design and build backend services, APIs, data processing workflows, integrations, and product-facing capabilities
• 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
• Make practical technical decisions and explain the tradeoffs clearly
• Help raise engineering standards through code reviews, design discussions, mentoring, and documentation
• Debug production issues and help teams get to durable fixes, not just short-term patches
• Use AI-powered tools effectively while maintaining high standards for correctness, security, and maintainability

• Direct 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 work on 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 senior engineers are expected to own problems, influence direction, and ship

• 5+ years of experience in a software engineering role
• Strong proficiency in one or more modern programming languages such as Python, Go, TypeScript, or SQL
• Experience designing and building backend services, APIs, distributed systems, data processing workflows, or production product features
• Strong understanding of relational databases such as Postgres
• Experience with cloud platforms such as AWS or GCP
• 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
• Ability to reason about system design, performance, reliability, security, and operational tradeoffs
• Experience leading technical work across a team without needing formal authority
• Strong communication skills in code reviews, design discussions, and cross-functional planning

• 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
• 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 and helping teams improve technical practices
• Experience evaluating AI-generated code and using AI tools as part of an engineering workflow

As an AI-forward engineering organization, we expect senior engineers to effectively leverage AI tools and understand foundational AI concepts to improve development efficiency and build AI-ready systems.

• 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 is a plus
• Ensure quality, observability, reliability, security, and performance in systems that use or support AI capabilities