Site Reliability Engineer - SRE

Senior Site Reliability Engineer at Drivetrain

Drivetrain is on a mission to empower businesses to make better decisions. Our financial planning & decision-making platform helps companies scale and achieve their targets predictably.

Drivetrain is a remote-first company headquartered in the San Francisco Bay Area. Founded in 2021 by a couple of ex-Googlers, Drivetrain is a fast-growing company on a trajectory for success with backing from leading venture capital firms.

Drivetrain provides a great culture for its employees to thrive in and be happy.

• 💜 Remote-friendly: Drivetrain brings together the best and the brightest, no matter where they are and provides them a great degree of autonomy. We trust our people.
• 🗣️ Open & transparent: We know that when our creators have access to all the information they need, their best work will emerge.
• 👏 Idea-friendly: We provide an environment to explore new ideas, to take risks, to make mistakes, and to learn, so you can succeed. Anyone in the company can come up with great ideas and become a catalyst for positive change. We let the best ideas win.
• 👥 Customer-centric: We follow a product-led growth strategy, continuously learning from our customers and collaborating to build the amazing software that Drivetrain is.

About the Role

As a Senior Site Reliability Engineer at Drivetrain, you will be a cornerstone of our engineering organization, ensuring our fast-growing SaaS platform remains highly available, performant, and secure. At this stage of our growth, scaling infrastructure efficiently while maintaining the rigorous security and reliability standards required for financial data is paramount. You will take ownership of our multi-cloud infrastructure, drive automation, champion observability, and collaborate closely with development teams to build a culture of reliability from code commit to production.

Key Responsibilities

Cloud Infrastructure & Orchestration

• Multi-Cloud Management: Architect, manage, and continuously optimize highly available cloud infrastructure across both AWS and GCP. Balance workload demands to ensure maximum cost-efficiency, scalability, and strict security compliance across both platforms.
• Advanced Kubernetes Orchestration: Lead the design, deployment, and management of scalable Kubernetes clusters. Utilize configuration management tools like Kustomize to enforce standardized, repeatable, and automated deployment configurations across all environments.
• Service Mesh & Security Integration: Implement and maintain service mesh technologies (e.g., Istio, Linkerd) to secure, control, and observe service-to-service communication. Drive container security best practices, including image scanning, runtime protection, and strict RBAC enforcement.

CI/CD & Automation

• Pipeline Engineering: Architect, maintain, and optimize robust CI/CD pipelines using Git and Jenkins. Focus on reducing deployment friction, accelerating release velocity, and enforcing automated testing and security gates.
• Infrastructure as Code (IaC): Treat infrastructure as software. Write, review, and maintain Terraform modules to provision and manage cloud resources predictably and safely.
• Operational Automation: Aggressively reduce operational toil. Develop robust Python scripts and tooling to automate routine maintenance, data backups, scaling operations, and system recovery processes.

Observability & Reliability

• Comprehensive Monitoring: Design and enhance our observability stack to provide deep, real-time insights into system health. Manage and scale tools including Prometheus, Grafana, ELK/EFK stack, AWS CloudWatch, and GCP Operations Suite.
• Reliability Engineering: Spearhead reliability initiatives critical to a scaling SaaS platform. Drive rigorous capacity planning exercises to stay ahead of growth.
• Incident Management & SLOs: Own the incident response lifecycle. Facilitate blameless postmortems to extract actionable learnings. Define, track, and enforce SLIs, SLOs, and SLAs, ensuring the platform consistently meets its reliability guarantees.

Collaboration & Leadership

• DevOps Culture: Act as an embedded reliability advocate. Collaborate closely with software engineers early in the development lifecycle to ensure applications are designed for deployability, scalability, and resilience.
• Continuous Improvement: Proactively identify system bottlenecks and architectural weaknesses. Contribute to process improvements, build internal developer tooling, and maintain comprehensive documentation to elevate team productivity and system understanding.

Required Proficiency & Qualifications

• Experience: 5+ years of hands-on experience in Site Reliability Engineering, DevOps, or Cloud Infrastructure roles, preferably within a fast-paced SaaS environment.
• Cloud Platforms: Deep, proven proficiency in AWS (EC2, EKS, RDS, VPC, IAM, S3) AND GCP (GKE, Compute Engine, Cloud SQL, IAM, Cloud Storage). Ability to navigate and optimize multi-cloud architectures.
• Containerization: Expert-level knowledge of Docker and Kubernetes, including advanced deployment strategies and lifecycle management.
• Automation/IaC: Strong programming skills in Python and extensive experience with Terraform.
• Observability: Hands-on expertise building dashboards and alerting systems using Prometheus, Grafana, and log aggregation stacks (ELK/EFK).
• Networking & Security: Solid understanding of cloud networking (VPC peering, load balancing, DNS) and zero-trust security principles in a containerized environment.

Sounds exciting? Apply at careers@drivetrain.ai. It may just be the next best decision you’ve ever made!

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

Key skills/competency

• Site Reliability Engineering
• DevOps
• Cloud Infrastructure
• AWS
• GCP
• Kubernetes
• Docker
• Terraform
• Python
• Observability