Senior Software Engineering Manager

Key Responsibilities

• Provide strategic direction and coaching to direct reports (senior engineering managers), fostering a high-performance culture focused on innovation, delivery excellence, and cross-functional collaboration.
• Architect and deliver cloud-native digital platforms for real-time ingestion, processing, and analytics of industrial data.
• Data Quality Oversight: Establish and enforce multi-level data quality standards (e.g., completeness, conformity, consistency, accuracy) across platform components.
• Oversee the design and deployment of digital twins, 2D/3D simulations, and real-time visualization platforms (Unity, Unreal Engine, Omniverse).
• Drive Industrial IoT (IIoT) initiatives connecting machines, sensors, and edge devices with scalable software solutions.
• Champion adoption of AI/ML, computer vision, robotics, and predictive analytics for manufacturing efficiency and automation.
• Partner with manufacturing experts to bridge OT (Operational Technology) and Enterprise Systems ERP etc.
• Ensure solutions meet enterprise standards for cybersecurity, scalability, and interoperability.
• Collaborate with leadership on product strategy, technical roadmaps, and vendor/partner ecosystems (NVIDIA, Siemens, Dassault, PTC).
• Partner with product owners, data scientists, architects, and business leaders to translate strategic goals into technical deliverables. Ensure platform capabilities support predictive supply, dynamic scheduling, and digital twin use cases
• Drive engineering excellence with best practices in Agile, DevOps, CI/CD, clean code, and system design.
• Platform Governance: Define ownership models, support structures, and prioritization frameworks to ensure long-term sustainability and scalability of the platform.
• Ensure Talent Development and Organizational Scalability
  
  Lead hiring, onboarding, and development of engineering talent. Design organizational structures that support agility, scalability, and future transitions across global teams

Required Qualifications

• 20+ years of experience in software engineering, with 10+ years in engineering leadership roles.
• Proven track record of delivering enterprise-scale digital platforms, preferably in manufacturing, supply chain, or industrial domains.
• Demonstrated ability to lead cross-functional teams across engineering, analytics, product, and business domains.
• Proven expertise in cloud-native architectures (AWS/Azure/GCP), microservices, and data engineering (Kafka, Spark, Flink, time-series databases).
• Hands-on experience with IIOT, digital twins, AI Agents, simulations, or CAD/CAE integrations in a manufacturing context.
• Composable Architecture: Design reusable data objects and services that support modularity, abstraction, and rapid feature evolution.
• Deep understanding of Industry 4.0 technologies IIoT protocols (MQTT, OPC UA), MES/ERP integration, robotics, automation systems.
• Experience with NVIDIA Omniverse, Unity, Unreal Engine, or similar simulation/visualization platforms.
• Strong background in AI/ML and analytics for predictive maintenance, quality control, or optimization.
• Data Integration Across Manufacturing Systems - Experience integrating ERP, MES, PLM, and IoT systems into a unified digital ecosystem. Ability to normalize and contextualize data from diverse sources support real-time decision-making and analytics
• Excellent leadership, stakeholder management, and communication skills with ability to drive cross-functional collaboration.
• Demonstrated ability to define platform strategy, lead architectural decisions, and align engineering execution with business goals.
• Strong organizational design and talent development skills, including hiring, mentoring, and performance management.

Nice-to-Have Skills

• Experience in high-performance computing (HPC), GPU programming (CUDA), or real-time rendering.
• Knowledge of cybersecurity for OT/IT systems.
• Exposure to AR/VR/XR applications for training or industrial collaboration.
• Prior work with autonomous robotics, AGVs/AMRs, or cyber-physical systems.
• Published work, patents, or contributions in digital manufacturing/Industry 4.0 technologies.
• Platform-as-a-Product Mindset -- Experience evolving platform capabilities based on user feedback and business needs, with a focus on composability and abstraction over siloed systems.