Software Cloud Engineering Team Lead
Architect scalable cloud platforms and build the team behind dependable StoneAge automation.
The opportunity
StoneAge automation depends on more than rugged equipment. Its cloud platforms, applications, data pipelines, and connected systems must perform as one secure, reliable ecosystem in demanding industrial environments.
The Software Cloud Engineering Team Lead owns the technical direction and engineering capability behind that ecosystem. You will lead the people, architecture, and operating practices that enable StoneAge software to scale—from field devices and edge systems through cloud services and user experiences.
This is a hands-on technical leadership role for someone who can make sound architectural tradeoffs, develop engineers, improve delivery discipline, and remain calm when production systems do not behave as expected.
Your mandate: Build a disciplined software engineering team that delivers secure, high-throughput cloud platforms and full-stack applications safely, reliably, and at scale.
What you will own
Architecture and scale. Set technical direction across cloud infrastructure, backend services, applications, networking, data, security, observability, and edge integration. Design for hundreds or thousands of connected systems—not only today’s deployment.
Engineering discipline. Establish practical standards for architecture, code quality, testing, documentation, traceability, code review, CI/CD, release readiness, and technical debt. Use discipline to improve speed and predictability.
Team capability. Set clear expectations, give useful feedback, create ownership, and develop engineers who can reason beyond their immediate specialty. Use Agile and the SDLC to create alignment—not administrative ceremony.
Field-to-cloud reliability. Design for real operating conditions, including intermittent networks, disconnected devices, changing bandwidth, unexpected sensor behavior, and complex hardware/software interactions.
Data platforms. Guide secure, efficient movement and storage of operational and telemetry data. Select distributed-processing, relational, NoSQL, and AI-oriented technologies according to the problem being solved.
Production operations. Create clarity during incidents, protect customers and operations, establish facts, drive root-cause resolution, and ensure failures strengthen both the system and the team.
Measurable outcomes. Make availability, latency, throughput, database performance, deployment stability, escaped defects, security risk, and delivery predictability visible—and act when they move in the wrong direction.
How you will lead
Make difficult architectural tradeoffs and explain technical risk in language that product, engineering, and business partners can use.
Know when to go deep technically and when to empower an engineer to own the solution.
Balance speed to market with long-term reliability, security, maintainability, and data integrity.
Build accountability and learning without blame, unnecessary bureaucracy, or dependence on heroic effort.
Own the outcome—not merely the pull request, sprint, meeting, or line of code.
Technical foundation
You need enough technical depth to lead engineers credibly, challenge architectural decisions, and engage directly when the situation demands it. The environment includes:
Languages: Java, Python, and JavaScript/TypeScript.
Applications: Modern backend services, including Spring Boot, and frontend frameworks such as Angular, React, or Flutter.
Cloud and distributed systems: Cloud-native networking, security, scalability, fault tolerance, observability, CI/CD, high-throughput messaging, and low-latency processing.
Data: Relational and distributed/NoSQL systems; technologies may include PostgreSQL, Bigtable, Cassandra, MongoDB, Pub/Sub, Dataflow, Flink, Beam, or Spark.
AI integration: Practical judgment about AI workloads, model mechanics, development tools, and vector data architectures—and where they genuinely create value.
Engineering operations: Modern development and delivery tools such as IntelliJ, VS Code, Jira, Jira Service Management, and Confluence.
Technology will change. Your ability to reason about systems and select fit-for-purpose solutions matters more than allegiance to a particular tool.
What you will bring
Demonstrated technical leadership across cloud platforms, distributed systems, full-stack applications, or closely related software environments.
Experience leading engineers through architecture decisions, delivery commitments, production incidents, and continuous improvement.
Strong understanding of secure cloud architecture, software delivery practices, data integrity, observability, and operational reliability.
Ability to decompose complex problems into understandable, testable components and make decisions without perfect information.
Direct, constructive communication and the ability to align technical and nontechnical stakeholders around risk, priorities, and outcomes.
A record of building ownership, raising standards, and helping teams deliver increasingly difficult work predictably.
Preferred experience
Industrial automation, connected equipment, edge computing, IoT, embedded systems, or other field-deployed technology.
C++ or Dart, vector databases, AI-oriented architectures, or large-scale telemetry platforms.
Leading across frontend, backend, cloud/platform, and embedded or hardware-adjacent teams.
Success in the first six months
Your impact should be visible in how the software organization operates. StoneAge should have:
A clear view of its cloud and application architecture, technology stack, technical debt, security exposure, and scaling risks.
Consistent engineering standards, disciplined code review, automated testing, and defined release-readiness criteria across frontend and backend teams.
More credible sprint commitments and increasingly predictable delivery timelines.
Meaningful visibility into availability, latency, performance, defects, and release stability, with field software issues trending downward.
Stronger alignment among cloud, application, embedded, and field systems—and a software team increasingly acting as owner of a mission-critical platform.
The standard
Great software engineering leadership is measured by what people can confidently depend on because the team exists: systems that stay up, data that can be trusted, software that ships predictably, architecture that can scale, engineers who take ownership, and technology that works when it reaches the field.