Site Reliability Engineer II

Mimecast•Columbus, OH

4d•Hybrid

About The Position

Mimecast is seeking a Site Reliability Engineer II to join their Governance Compliance and Insights (GCI) pillar. This team is central to how Mimecast builds, ships, and operates its cloud platform, focusing on cloud infrastructure design and evolution on AWS, CI/CD pipeline management, and observability. The team believes in continuous deployment, empowering builders to own their creations, and prioritizing process and standardization. Mimecast is an AI-First engineering organization, actively leveraging AI-powered development tools across all engineering facets and seeking leaders who champion AI adoption. The role involves hands-on infrastructure work, debugging, and creating repeatable, self-service patterns for engineering teams. The primary focus is on provisioning infrastructure and debugging complex distributed systems, with CI/CD expertise being highly valued. Observability knowledge is also beneficial, though increasingly centralized. The engineer will work in a small team, partnering with product engineering squads, and will rotate between infrastructure build-outs, pipeline improvements, and incident investigations.

Requirements

Hands-on Kubernetes experience — you can deploy, operate, and debug workloads on Kubernetes. You do not need to be an expert, but you should be comfortable in it.
Terraform experience — you have written and maintained non-trivial Terraform. Again, expertise is not required; pragmatic competence is.
Familiarity with setting up CI and deployment automation. Experience with GitHub Actions is strongly preferred; experience with Jenkins, GitLab CI, or similar is transferable.
A working understanding of observability fundamentals — logs, metrics, and traces — and how they are used during incident response.
Networking knowledge at a practical level: security groups, TLS certificates, DNS, load balancing, and how traffic actually flows through a cloud environment.
Experience working with relational databases is helpful — PostgreSQL is ideal but not required.
Experience with AWS or similar cloud platforms
Demonstrable experience prompting and working with AI coding and reasoning tools to produce real, shipped work.
Good judgement about when AI output is trustworthy and when it needs verification — particularly for infrastructure code and production-impacting changes.
An interest in applying AI tooling to SRE problems: incident triage, runbook generation, log analysis, Terraform authoring, and similar.
You collaborate well across teams — you can work with product engineers, platform teams, and security peers without friction.
You have a bias for action and problem-solving, and you prefer shipping small, frequent improvements over big-bang changes.
You are comfortable saying “let’s standardize this” rather than building a one-off, even when the one-off would be faster today.
You communicate clearly in writing — our teams are distributed, and async clarity matters.

Nice To Haves

Experience with GitHub Actions is strongly preferred; experience with Jenkins, GitLab CI, or similar is transferable.
Experience with PostgreSQL is ideal but not required.
Experience with AWS or similar cloud platforms

Responsibilities

Stand up and evolve AWS infrastructure using Terraform, with a strong bias toward reusable modules and paved-road patterns over bespoke solutions.
Operate and improve Kubernetes-based workloads — deployments, scaling, networking, and the platform glue that makes them boring to run.
Build and maintain CI/CD pipelines (GitHub Actions preferred) that give engineering teams fast, safe, auditable paths to production.
Partner with product squads to enable self-service access to their own infrastructure, databases, and pipelines — with the guardrails, auditability, and standards that make that access safe.
Debug production issues across the stack: networking, DNS, certificates, container orchestration, CI pipelines, and application-level behaviour.
Instrument services with appropriate logs, metrics, and traces, and help teams adopt the observability standards our platform teams define.
Contribute to runbooks, automation, and standards that reduce toil and one-off work — if you fix it twice, codify it the third time.
Use AI tooling pragmatically and at a professional level: to accelerate code generation, infrastructure design, debugging, documentation, and review. We expect engineers to be AI-first in how they approach their craft.