Senior / Staff Software Engineer - Integration Team

Stellar Cyber

17h•$160,000 - $245,000

About The Position

The Senior or Staff Software Engineer will own one or more integration domains on an automation-driven SecOps platform supporting AI-driven SIEM, NDR, Open XDR, and Multi-Layer AI. The role delivers production-grade integrations and advances the platform architecture to support scale. Solutions include microservices and traditional and AI-native integration patterns such as REST, GraphQL, streaming APIs, MCP, and LLM-powered automation. The engineer also designs, develops, and maintains parsing and normalization components that convert security data from hundreds of sources into a unified schema for detection and analytics engines. The role includes end-to-end ownership of integration roadmaps, architecture decisions, and delivery; evolving shared integration frameworks; exercising judgment over schema mapping and data normalization; partnering with detection and data teams; and mentoring junior engineers. AI tools are used to accelerate design, development, testing, code review, parser creation, and repetitive engineering tasks, while the engineer applies judgment to validate AI output and supports adoption across the team. Successful finalists will be invited to an on-site interview.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
At least 5 years of backend software development experience, including integration, data parsing, or log processing work.
At least 1 year as a technical lead or proven experience taking full ownership of a project.
Strong proficiency in at least one general-purpose programming language, such as Python, Go, Java, or C++, with a record of producing production-quality code.
Experience with API design and integration, including REST, GraphQL, or streaming APIs, and secure API patterns such as OAuth, API keys, and rate limiting.
Understanding of common log and data formats, including JSON, XML, CSV, syslog, key-value, and unstructured text, as well as regular expressions, data normalization, and schema design.
Demonstrated regular use of AI tools such as Copilot, Cursor, Claude, or ChatGPT to accelerate engineering workflows in production work, with sound judgment about when AI output requires human expertise.
Strong problem-solving skills and clear communication with engineers and non-technical stakeholders.

Nice To Haves

Experience in cybersecurity domains such as SOAR, EDR, SIEM, XDR, firewalls, IDS/IPS, or cloud security services, and familiarity with the data those tools emit.
Experience building and operating microservices in production.
Hands-on experience with large-scale data processing, real-time pipelines, or event-driven architectures such as Kafka or Spark.
Background in distributed systems, message queues such as Kafka or RabbitMQ, or orchestration frameworks such as Celery or Airflow.
Familiarity with AWS, Azure, or GCP and their logging services, such as CloudWatch, Azure Monitor, or Cloud Logging, as well as Docker, Kubernetes, or Helm.
Experience with AI service integration, including LLM APIs or embedding services, or AI-native patterns such as MCP, function calling, and agent orchestration; alternatively, experience building systems that incorporate LLMs or AI services into workflows or product features.
Hands-on experience using LLMs for log analysis, parser generation, or schema inference, or building internal tooling that applies AI to data engineering workflows.

Responsibilities

Own one or more integration domains end-to-end, including roadmap, architecture decisions, trade-offs, and delivery.
Evolve shared integration frameworks, including parser frameworks, by identifying reusable patterns, designing primitives, improving performance, and increasing reliability and testability.
Make design decisions about schema mapping, normalization, ambiguous or malformed data, generalization versus specialization, and framework evolution.
Partner with detection and data teams to ensure ingested data supports downstream use cases.
Mentor junior engineers through design discussions and code reviews.
Use LLM-based coding assistants, AI-driven testing, and automated code review to accelerate design and development.
Use LLMs to analyze unfamiliar log samples and help generate initial parsing rules and integrations, validating the output with human judgment.
Automate repetitive engineering tasks such as builds, deployments, monitoring, incident triage, parser regression testing, and schema diffing.
Champion AI adoption across the team, help colleagues use AI tools, build internal tooling where useful, and measure efficiency gains.