Staff Software Engineer, Cloud FinOps - Attentive

Job Title: Staff Software Engineer, Cloud FinOps
Job Location: United States
Job Listing URL: https://jobs.lever.co/attentive/60d0e368-960c-4727-9da0-d6d36813cead
Job Description: Attentive® is the AI-powered mobile marketing platform transforming the way brands personalize consumer engagement. Attentive enables marketers to craft tailored journeys for every subscriber, driving higher recurring revenue and maximizing campaign performance. Activating real-time data from multiple channels and advanced AI, the platform personalizes content, tone, and timing to deliver 1:1 messages that truly resonate.

With a top-rated customer success team recognized on G2, Attentive partners with marketers to provide strategic guidance and optimize SMS and email campaigns. Trusted by leading global brands like Neiman Marcus, Samsung, Wayfair, and Dyson, Attentive ensures enterprise-grade compliance and deliverability, supporting trillions of interactions across more than 70 industries. To learn more or request a demo, visit www.attentive.com or follow us on LinkedIn, X (formerly Twitter), or Instagram.

Attentive’s growth has been recognized by Deloitte’s Fast 500, Linkedin’s Top Startups and Forbes Cloud 100 all thanks to the hard work from our global employees!

About the Role
Our Platform Infrastructure team is the backbone of everything we do at Attentive, providing a resilient and cost-effective platform that seamlessly handles billions of events from over 100 million customers daily. We own everything from compute, persistence, and networking to observability and deployments. Joining our team offers a high-growth career opportunity to collaborate with some of the world’s most talented engineers in a high-performance, high-impact culture.

As a Staff Engineer in Production Engineering, you will have the opportunity to work across all of the engineering teams at Attentive, focusing on implementing and driving cost optimizations across our Software Stack. You will collaborate and work with engineers, product managers and our finance team. Your work will drive millions of cost savings, provide enhanced visibility and improve our cost model.
What You'll Accomplish
Spearhead FinOps initiatives to increase cost transparency and optimize resource utilization across teams
Provide guidance and mentorship to engineers on cost-optimization best practices
Collaborate with teams across the organization to implement cost-saving measures
Analyze, troubleshoot, coordinate, and resolve complex infrastructure issues
Proactively lead infrastructure initiatives that have a company-wide impact
Partner with engineering teams to influence cost-effective architectural and design choices
Develop automation workflows to enhance team productivity and efficiency
Foster a culture of continuous improvement by encouraging innovative thinking and challenging established norms
Your Expertise
Bachelor's degree in Computer Science, Engineering, or related field; Master’s degree preferred
2+ years of experience in FinOps or cloud cost management
5+ years of experience in cloud infrastructure management, development or related roles
Proficiency in some of the following technologies: AWS, Datadog, Kubernetes, Vantage, Terraform, Java
Strong analytical and problem-solving skills with a keen attention to detail
Excellent communication and leadership abilities
Ability to work collaboratively in a cross-functional team environment
What We Use
Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS
Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS
Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas