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Lead Tech SRE

Kestra Technologies Sourced

Europe, Germany Full-time Not specified

No account needed — this role is applied for on the employer’s own site.

About the role

About Kestra Kestra is the universal orchestration platform: open source, declarative, and designed to orchestrate data pipelines, IT automation, business workflows, and AI/agentic systems. Trusted by over 10,000 organizations worldwide, including JPMorgan Chase, Bloomberg, FILA, and Crédit Agricole, Kestra orchestrates mission-critical workloads at scale. The open-source project has close to 30,000 GitHub stars, hundreds of contributors, and a fast-growing global community. About the role Kestra Cloud is our fully managed SaaS edition of Kestra. Each customer gets a dedicated Kestra instance on dedicated infrastructure, so the platform is a fleet of isolated stacks rather than one shared cluster. It is in Beta today, running production workloads for early customers, with General Availability planned for 2027. Each instance runs arbitrary user code (scripts, containers, queries against their own systems) through hundreds of plugins, and the fleet grows with every new customer. Provisioning, upgrading, and observing dozens to hundreds of identical stacks without drift is the core problem. We're looking for a Lead Tech SRE to own Kestra Cloud end to end: the architecture and specs, the delivery, the monitoring, and the continuous improvement of the platform. You decide how the platform should evolve, write it down, split it into work, and ship it with your team. You would lead a team of three, yourself included. That means you stay hands-on (a large share of your week is Terraform, Kubernetes manifests, and incident work) while setting the technical direction, reviewing designs, and dispatching work to two SREs. You work closely with the CTO, the platform engineering teams building the Kestra core, and our security engineer. This is an engineering leadership role, not a people-management-only one. What you would do Architecture & specs Own the target architecture of Kestra Cloud on GCP: the per-customer dedicated stack (cluster, network, databases, queues), how it is templated and versioned, regions, and the path from Beta to GA. Write the specs (design docs, RFCs, ADRs) that turn product needs into infrastructure work, and defend the trade-offs: cost per customer vs isolation level, GCP project vs cluster vs node pool per customer, managed service vs self-run. Define SLOs and error budgets per service and per tier, and make them the basis for prioritization. Delivery Break specs into work and dispatch it across the team, then review and unblock. You set the bar for Terraform modules, Helm charts, and GitOps conventions. Automate the customer instance lifecycle: provisioning, upgrades, scaling, backups, migration, and decommissioning, with no manual steps. Own the release path of Kestra Cloud with ArgoCD and GitHub Actions: progressive rollouts, rollback, and upgrade safety across the whole fleet of customer instances. Monitoring & operations Build the observability stack (metrics, logs, traces, alerting) so that issues are caught before customers report them. Run on-call and incident response for the team: rotation, runbooks, blameless postmortems, and follow-ups that actually ship. Plan capacity for a workload that is bursty by nature, and keep autoscaling honest. Continuous improvement Drive cost efficiency (FinOps) on GCP: per-customer cost visibility, right-sizing, committed use, and spot where it fits. Harden the platform with our security engineer: network policies, workload identity, secrets, and audit trails toward SOC 2 expectations. Feed back into the product: when Kestra itself makes the cloud hard to run, you write the issue, propose the fix, and work with core engineering to land it. Our Tech Stack Cloud: GCP (GKE, Cloud SQL, GCS, Cloud Load Balancing, IAM / Workload Identity) Infrastructure as Code: Terraform, Helm Orchestration: Kubernetes, Docker Deployment & Repository: GitHub Actions, ArgoCD Monitoring & Logs: OpenTelemetry, Elastic, Prometheus, Grafana Datastore: PostgreSQL, Elasticsearch Qu

Skills

Engineering

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