Senior DevOps Engineer - Data Platform (Cortex)

We're looking for a Senior DevOps Engineer with a strong Developer Experience (DevEx) mindset, someone who genuinely enjoys making developers more productive by building and improving the tools, infrastructure, automation, and workflows they use every day.
You will work across CI/CD, developer tooling, automation, infrastructure, observability and productivity. Your primary focus will be making development and delivery faster, simpler, more reliable, and more cost-effective.
We need someone who can independently identify developer pain points, proactively discover opportunities for improvement, and turn them into scalable engineering solutions. You will be expected to measure the impact of your work and continuously improve critical metrics such as CI feedback time, pipeline reliability, deployment time, infrastructure cost, and overall developer productivity.
This role is a perfect fit for someone who thinks far beyond just "keeping the infrastructure running" and is excited about revolutionizing the entire engineering experience.
Your Impact:
You will tackle high-impact engineering challenges across our platform. Representative areas you will own and work on include:
Optimize CI/CD Pipeline Velocity & Stability:
Improve CI Stability: Leverage AI to automatically detect recurring test failures, dynamically disable problematic tests, and open Jira tickets for follow-up.
Automate Workflows: Utilize AI to automate merge conflict resolutions and keep the merge train moving smoothly.
Accelerate CI Velocity: Architect workflows to parallelize work both across jobs and within jobs. Introduce highly effective caching strategies and local Artifactory mirrors to drastically reduce build and dependency-fetch times.
Optimize CD: Reduce container image sizes and accelerate deployment times. Partner with QA to introduce and streamline automated regression testing against tenants.
Drive Repository Security & Maintenance:
Improve Security Posture: Automate the identification and remediation of Go and Python dependencies with known CVEs.
Automate Maintenance: Design systems to automatically handle Go and Python version upgrades across the codebase.
Ensure Performance: Automate performance testing to proactively detect and alert on performance regressions before they hit production.
Cloud Infrastructure & Cost Engineering:
Reduce Infrastructure Costs: Identify and implement innovative opportunities to reduce overall infrastructure and resource consumption.
Resource Efficiency: Introduce mechanisms to make our "slim tenants" even more resource-efficient for development and testing.
Smart Observability: Re-architect our logging strategy by streaming GCP logs to more cost-effective sinks instead of relying exclusively on Cloud Logging.
IaC Governance: Act as a gatekeeper for infrastructure quality by reviewing Terraform Merge Requests (MRs) for correctness, efficiency, maintainability, and strict adherence to engineering standards.