Software Cloud Engineering Team Lead

StoneAge Tools

10d•$120,000 - $140,000•Onsite

About The Position

StoneAge Holdings is the global leader in designing and manufacturing high-pressure waterblast tooling and automated, IoT-enabled/robotic equipment, based in Durango, CO, with five subsidiaries in four countries. Think squirt guns on steroids run by humans and robots! StoneAge Holdings includes StoneAge Tools and Warthog Nozzles, trusted brands known worldwide for their durability, performance, and innovation in industrial cleaning applications. We are an innovative, employee-owned company that aims to change our industry and the world through advancing technical products and services – and with our unique, people-centric culture. 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.

Requirements

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.
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.

Nice To Haves

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.

Responsibilities

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.