Managing 144 engineers across 24 squads, delivering 99.99% SLA and Elite DORA performance.
This repository provides a deep dive into my approach to platform operations at scale. It showcases the frameworks, processes, and technologies I use to manage large, distributed teams and deliver world-class reliability, performance, and efficiency.
- Automation Everywhere: We automate everything from infrastructure provisioning to application deployment to incident response. This allows us to scale our operations without scaling our team, and it reduces the risk of human error.
- Self-Service Platforms: We build self-service platforms that empower our developers to deploy and manage their own applications. This reduces the burden on our platform team and allows our developers to move faster.
- Data-Driven Everything: We use data to drive our decisions and to measure our success. We have a comprehensive observability stack that gives us deep insights into the performance of our platform, and we use this data to identify areas for improvement.
- Continuous Improvement: We are constantly looking for ways to improve our platform and our processes. We have a regular cadence of retrospectives and reviews, and we use the feedback from these sessions to drive continuous improvement.
Disclaimer: These are examples of common industry challenges and their optimal suggested solutions, preserving confidentiality by not publishing internal technical stacks or proprietary information.
Problem: In large engineering organizations, it's common to see configuration drift between local, staging, and production environments. This leads to "it works on my machine" issues, slowing down the development lifecycle. Monolithic CI/CD pipelines often become a major bottleneck, with long queue times and complex, brittle scripts.
Optimal Solution: Adopting Platform Engineering principles is key. The internal platform should be treated as a product with developers as customers. A "Golden Path" using standardized templates and a self-service portal is a best practice. Key actions include implementing an Internal Developer Platform (IDP) using tools like Backstage, standardizing environments using technologies like DevContainers, and moving from a centralized, monolithic setup to a federated model using tools like GitHub Actions where each team owns their pipelines.
Problem: Teams often use a fragmented set of monitoring tools, leading to data silos. Without a unified view of system health, incident response is slow, and on-call engineers suffer from significant alert fatigue due to low-priority notifications.
Optimal Solution: Establishing an Observability Center of Excellence (CoE) helps centralize strategy and tooling, providing "Observability-as-a-Service." Standardizing on OpenTelemetry (OTel) for all applications and infrastructure creates a single, vendor-agnostic pipeline for logs, metrics, and traces. While allowing teams to use their preferred visualization tools, all telemetry data should be routed to a central platform for analysis, correlation, and AIOps-driven alerting. Shifting from system-level alerts to user-centric SLOs and Service Level Indicators (SLIs) drastically reduces alert noise.
Problem: Ad-hoc incident response processes lead to chaos. It's often unclear who is in command, communication is scattered, and postmortems can devolve into finger-pointing, which discourages transparency and learning.
Optimal Solution: Implementing a formal SRE-based Incident Management Framework is crucial. Establishing clear roles for every incident (Incident Commander, Communications Lead, Operations Lead) creates clear lines of authority and communication. Using dedicated incident management tools automates the creation of channels, video calls, and status pages. Championing and training teams on blameless postmortems is essential, shifting the focus from "who caused the problem?" to "what systemic factors allowed this to happen?"
Problem: Multi-cloud strategies can lead to duplicated effort, inconsistent security postures, and specialized knowledge silos. Managing Kubernetes clusters at scale across multiple clouds can become operationally unsustainable.
Optimal Solution: Implementing a Cloud-Native Control Plane abstracts away the complexity of the underlying infrastructure. Building a library of high-level, reusable IaC modules provides a simplified, opinionated interface for developers to provision resources, regardless of the target cloud. Standardizing on a GitOps tool like ArgoCD or Flux for continuous delivery to Kubernetes is a best practice. For Kubernetes provisioning, adopting Cluster API (CAPI) allows for the management of a fleet of clusters across different clouds using a unified, declarative API.
Problem: As engineering teams scale, onboarding new hires can become slow and inefficient. Critical knowledge often remains trapped in the heads of a few senior engineers, creating key-person dependencies and slowing down innovation.
Optimal Solution: Implementing a "Documentation-as-Code" and Knowledge Sharing Culture is vital. Using an IDP to create a single portal for all technical documentation, architectural decision records (ADRs), and runbooks is highly effective. Mandating the use of ADRs for all significant technical decisions creates a historical log of why decisions were made. Sponsoring the creation of internal "Tech Guilds" and a regular tech talk series fosters a community of practice and accelerates knowledge dissemination.
- LinkedIn: linkedin.com/in/benlakhoua
- Email: mo@metafive.one