Integration Software Engineering Manager

Join our team as an Integration Software Engineering Manager, where you will lead and develop a team of software engineers, stay hands-on with the work, guide delivery and collaboration, and promote the practical use of AI in development. You will have the opportunity to make a significant impact in a fast-paced, evolving environment.

• Lead and develop a team of software engineers, providing day-to-day leadership, coaching, feedback, and support.
• Participate in design discussions, code reviews, troubleshooting, and technical decision-making, while contributing directly when needed.
• Work with Product Management and partner teams to clarify priorities, scope work, and establish realistic plans, balancing new integration development with maintenance.

• A track record of coaching engineers and delivering reliable software in an evolving environment
• A hands-on leadership style and the willingness to remain technically engaged with the team
• Strong communication, prioritization, problem-solving, and cross-functional collaboration skills
• A practical understanding of microservices, distributed applications, or cloud-based systems
• A bachelor's degree in Computer Science, Engineering, or a related field
• Solid backend software-engineering experience in Python, Go, Java, or a comparable language
• The ability to translate product or customer needs into clear, achievable engineering work
• Experience managing or formally leading software engineers in a first-line leadership capacity
• Experience building, integrating, or operating production software and APIs
• Cybersecurity products or domains such as SIEM, SOAR, EDR, NDR, XDR, or security automation
• API and integration patterns such as REST, GraphQL, webhooks, streaming APIs, OAuth, or rate limiting
• Message queues, event-driven systems, or workflow orchestration technologies
• AWS, Microsoft Azure, Google Cloud Platform, containers, or Kubernetes
• Experience working with globally distributed engineering teams
• Large-scale data processing, observability, incident management, or site-reliability practices
• AI-assisted engineering tools, LLM APIs, function calling, MCP, or agent-based workflows