APM Software Team Leader

Role overview Lead software delivery and technical direction for an asset performance product that helps operators manage fleets of grid-connected equipment. The work combines analytics-heavy product development with team leadership, supporting predictive insights, maintenance planning, and risk optimization at scale.

Responsibilities - Lead a distributed engineering team responsible for product features, releases, maintenance, and ongoing improvements. - Plan work across teams with project management and product partners, translating requirements into technical plans. - Guide design and implementation decisions while maintaining software reliability, quality, and maintainability. - Coordinate with architecture, quality assurance, user experience, and DevOps teams to align with shared practices and platform design. - Ensure effective engineering processes for code review, testing, documentation, and releases; manage technical risks and debt. - Support investigation of complex technical issues and customer problems when needed. - Hire, onboard, coach, and develop team members while encouraging ownership, collaboration, and continuous improvement. - Align stakeholders across functions and time zones, communicating technical decisions clearly to technical and non-technical audiences.

Requirements - Bachelor’s or master’s degree in software engineering, computer science, or a similar technical discipline. - Strong software development background and experience in technical or people leadership, including leading distributed or international teams. - Backend or server development experience with Java, C#, or similar technologies. - Experience with distributed systems, client-server architectures, or large-scale device management, as well as databases, APIs, and integrations. - Experience working in Agile or iterative development environments. - Background in asset performance management, predictive analytics, or condition monitoring for industrial assets. - Experience with maintenance, risk-based, or fleet-level optimization, and with reliability methods such as RCM or FMEA. - Hands-on experience or strong technical understanding of production AI/ML, such as predictive maintenance, anomaly detection, or remaining useful life estimation. - Strong analytical, coordination, stakeholder management, and communication skills.

Nice to have - Experience with industrial software, IoT or device management, deployment and update mechanisms, cybersecurity, telemetry pipelines, time-series analysis, or digital twins. - Experience operationalizing ML models, including monitoring and retraining; familiarity with utility assets or relevant industry standards. - Coaching experience and a practical approach to balancing delivery timelines with technical quality.

Benefits and work setup - Remote position. Relocation assistance is not provided.