Senior / Staff Software Engineer - Integration Team

Join a fast-growing global leader in cybersecurity, trusted by some of the biggest names in the industry. In addition to some of the world’s largest enterprises and government agencies, more than 30% of the world’s top MSSPs rely on our platform. We’re at the forefront of protecting organizations against sophisticated cyber threats using cutting-edge AI and automation technologies. Our culture is built on diversity, openness, and collaboration, fostering creativity and innovation that drives real impact in the market.

We are looking for a Senior or Staff Software Engineer to take ownership of one or more integration domains on our Automation-Driven Open & Unified SecOps Platform (powering our AI-driven SIEM, NDR, Open XDR, and Multi-Layer AI).

This role requires delivering robust, production-grade integrations that support our growing customer base, while driving the architecture forward so the platform can scale to the next level. You will deliver microservice-based solutions spanning both traditional and AI-native integration patterns—REST, GraphQL, streaming APIs, MCP, and LLM-powered automation—connecting our platform with a wide range of cybersecurity tools and third-party services. You will also design, develop, and maintain the parsing and normalization components that turn raw security data from hundreds of sources into the unified schema our detection and analytics engines rely on.

Please note, as part of our interview process, we will be inviting successful finalists for an on-site interview to meet with our team. 

We need someone who focuses on the work AI cannot do—and uses AI to amplify everything else.

The work only you can do

• →Own one or more integration domains end-to-end: roadmap, architecture decisions, trade-offs, and delivery. You identify what needs solving, scope it, and drive it to completion.

• →Own and evolve shared integration frameworks, such as the parser framework: identify patterns worth abstracting, design the primitives other engineers build on, improve performance on hot paths, and raise the reliability and testability bar across the team’s output.
• →Make the design calls that require judgment—schema mapping, normalization trade-offs, how to handle ambiguous or malformed data, when to generalize versus when to special-case, when to evolve the framework versus work around it.
• →Partner with detection and data teams to make sure ingested data serves downstream use cases, not just passes validation.
• →Mentor junior engineers through design discussions and code reviews, helping them grow into independent owners.

• →Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• →5+ years of backend software development experience, including integration, data parsing, or log processing work, with 1+ years as a technical lead or proven experience in taking full ownership of a project.
• →Strong proficiency in at least one general-purpose programming language (e.g., Python, Go, Java, or C++), with a track record of writing production-quality code in it.
• →Experience with API design and integration (REST, GraphQL, streaming), including secure API patterns (OAuth, API keys, rate limiting).
• →Solid understanding of common log and data formats (JSON, XML, CSV, syslog, key-value, unstructured text), regular expressions, and data normalization and schema design.
• →Demonstrated ability to use AI tools (e.g., Copilot, Cursor, Claude, ChatGPT) to meaningfully accelerate engineering workflows—not just experimentation, but regular use in production work. You have the judgment to know when AI output is trustworthy and when it needs human expertise.
• →Strong problem-solving skills and clear communication with both engineers and non-technical stakeholders.

• Experience in cybersecurity domains (SOAR, EDR, SIEM, XDR, firewalls, IDS/IPS, cloud security services) and the kinds of data security tools emit.
• Experience building and operating microservices in production.
• Hands-on experience with large-scale data processing, real-time pipelines, or event-driven architectures (e.g., Kafka, Spark).
• Strong background in distributed systems, message queues (Kafka, RabbitMQ), or orchestration frameworks (Celery, Airflow, or similar).
• Familiarity with cloud platforms (AWS, Azure, GCP) and their logging services (CloudWatch, Azure Monitor, Cloud Logging), and containerized deployments (Docker, Kubernetes, Helm).
• Experience with AI service integration (LLM APIs, embedding services) or AI-native integration patterns—such as MCP (Model Context Protocol), function calling, or agent orchestration frameworks—or hands-on 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.