Staff Software Engineer - Integration
S
Stellar CyberCybersecurity
United StatesFull-TimeStaff
Salary150000 - 210000 USD per year
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Job Details
- Experience
- 8+ years
- Required Skills
- AWSGraphQLPythonGCPJavaKafkaKubernetesRabbitmqAirflowAzureGoRESTful APIsMicroservicesHelm
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
- 8+ years of backend software development experience
- 3+ years in a Staff-level or tech lead role
- Proficiency in Python, Go, or Java
- Strong foundation in building and operating microservices in production
- Deep experience with API design and integration (REST, GraphQL, streaming, LLM APIs, embedding services)
- Experience with secure API patterns (OAuth, API keys, rate limiting)
- Strong background in distributed systems
- Experience with message queues (Kafka, RabbitMQ)
- Experience with orchestration frameworks (Celery, Airflow, or similar)
- Demonstrated ability to use AI tools (Copilot, Cursor, Claude, ChatGPT) to accelerate engineering workflows
- Track record of mentoring engineers
- Excellent system design, debugging, and communication skills
- Experience in cybersecurity domains (SOAR, EDR, SIEM, XDR) (Preferred)
- Hands-on experience with large-scale data processing, real-time pipelines, or event-driven architectures (Preferred)
- Familiarity with cloud platforms (AWS, Azure, GCP) and containerized deployments (Kubernetes, Helm) (Preferred)
- Experience with AI-native integration patterns (MCP, function calling, agent orchestration frameworks) (Preferred)
Responsibilities
- Own one or more integration domains end-to-end: roadmap, architecture decisions, trade-offs, and delivery.
- Identify what needs solving, scope it, and drive it to completion.
- Make the architectural calls—system boundaries, API contracts, reliability targets, build-vs-buy trade-offs.
- Serve as a technical mentor to the engineering team—through design reviews, pairing sessions, and code reviews.
- Set technical direction and champion engineering rigor across the team: quality, documentation, and best practices.
- Accelerate design and development workflow using LLM-based coding assistants, AI-driven testing, and automated code review.
- Automate repetitive engineering tasks—builds, deployments, monitoring, incident triage—using AI and scripting.
- Champion AI adoption across the team.
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