Sr Engineering Manager, Cloud Infrastructure Engineering

About the role

The cloud infrastructure and developer platform teams set the foundation for how engineering delivers software at scale. They coordinate technical direction with security, compliance, and business stakeholders to balance speed, reliability, and cost. The role focuses on building durable platforms and shaping a culture of ownership and operational excellence.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Large-scale infrastructure roadmaps are defined and executed to improve developer productivity and platform reliability.

Cloud infrastructure on Amazon Web Services is architected and operated using modern cloud patterns to meet scalability and cost-efficiency goals.

Reliable, scalable, and secure production platforms are designed with strong networking, storage, observability, and security controls.

DevOps and Infrastructure as Code practices power CI/CD systems that automate software delivery while balancing technical debt and long-term investment.

Cross-functional teams align infrastructure priorities with product and business objectives through clear communication and tradeoff analysis.

Incident management processes are led to resolve escalations, drive post-incident reviews, and implement systemic reliability improvements.

High-performing engineers are recruited, coached, and mentored to build and retain strong infrastructure and platform teams.

Requirements

A bachelor's degree in Computer Science, Engineering, or a related technical field is required.

At least twelve years of hands-on software, infrastructure, or platform engineering experience is required.

At least four years of engineering management experience is required.

Experience building and operating highly available cloud-native platforms and distributed systems is required.

Hands-on work with Amazon Web Services and modern cloud architecture patterns is required.

Strong background in DevOps, Infrastructure as Code, CI/CD, automation, and platform engineering is required.

Ability to design cost-efficient and secure infrastructure for production environments is required.

Experience guiding technical strategy across multiple engineering teams and stakeholders is required.

Practical notes

Employment is at-will in San Francisco. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Engineers use cloud infrastructure, CI/CD pipelines, and DevOps tooling to ship reliable software. Data-driven decisions and observability guide platform investments. Collaboration across product, security, and operations is central to success. The role emphasizes ownership, transparency, and continuous improvement. Infrastructure decisions balance scale, reliability, and cost.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.