Lead Cloud Engineer for the Cloud Engineering Team, shaping technical strategy and improving AWS-based infrastructure, reliability, observability, CI/CD, security, and cost efficiency.
About this role
The Lead Cloud Engineer joins the Cloud Engineering Team, which supports infrastructure, reliability, and scalability across Mico’s new and legacy product portfolio. The role shapes a team or important project’s growth strategy and provides technical leadership across the organization.
Responsibilities include leading measurable technical and cross-functional projects; improving system stability and scalability; managing SLI/SLOs and error budgets; responding to incidents; standardizing RCA and blameless postmortems; improving observability for metrics, logs, and traces; and advancing developer productivity through CI/CD pipelines and release management. The role also addresses cloud-cost optimization, security requirements, and documentation. Its named infrastructure focus is AWS, with Infrastructure as Code using Terraform or AWS CDK.
Applicants need experience designing, building, and operating cloud-based systems, Infrastructure as Code, and CI/CD or DevOps pipelines. The posting also requires advanced debugging and troubleshooting, system-architecture understanding, technical decision-making for a team or project, and cross-functional consensus-building.
This is an AI-generated summary of the employer's original posting — details can be incomplete, out of date or simply wrong. Always confirm everything on the official posting before applying.
Similar jobs
Leads Data Platform governance and technical roadmaps, sets TiDB/Snowflake data-placement decisions, shifts heavy analytics from transactional databases, and guides customer and segment model redesign.
Lead server-side development and technical decisions for Loglass SaaS, including rearchitecture, performance improvements, workflow automation, and project leadership. Telework is included.
Advise clients on data strategy, build DWH and BI foundations, and use SQL, Python, machine learning and CRM analytics to improve marketing, forecasting and business operations.
Develop and expand AI for chemical risk management for global manufacturers, involving planning, design, development, and operation.
Design and implement machine-learning algorithms using tabular, natural-language, and image data, then work with consultants and software engineers to turn client solutions into usable software.