Job Description

We are looking for an experienced data platform engineer to own the event-streaming backbone behind the k0rdent-ai platform — the multi-tenant control plane for enterprise GPU infrastructure. Every cluster provisioned, every GPU-hour consumed, and every tenant action produces events that must move reliably from our services into analytics, billing, observability, and downstream systems. You will design and operate that pipeline end to end: the streaming platform itself, the connectors and change-data-capture flows that feed it, the schemas that keep producers and consumers compatible, and the automation that lets product teams onboard themselves without waiting on you. Working within an agile framework alongside engineering teams across the US, Europe, and India, you will make event data a dependable product surface rather than a best-effort side channel.

Main Responsibilities:

- Design, deploy, and operate the production streaming platform on Kubernetes — cluster lifecycle, topics, partitioning and retention strategy, capacity planning, and upgrades.

- Build and maintain change-data-capture and connector pipelines moving data between operational databases, the streaming layer, and analytical stores.

- Own schema governance — a schema registry, compatibility rules,



and versioning discipline so producer changes never silently break consumers.

- Develop custom connectors, transformations, and stream-processing logic in Java or Go where off-the-shelf components fall short.

- Build self-service onboarding so product teams can provision topics, schemas, and access through infrastructure as code rather than tickets.

- Automate the platform with Terraform and GitOps — no manually configured clusters, no undocumented topic definitions.

- Instrument the pipeline for production: metrics, lag and throughput monitoring, alerting, and dashboards that both the platform team and application owners can act on.

- Enforce security and multi-tenant isolation across the streaming layer — mTLS, authentication, topic-level RBAC, encryption, and audit trails.

- Guarantee delivery semantics that the business can rely on — ordering, idempotency, exactly-once where required, replay, and disaster recovery.

- Partner with backend and SRE teams on event schema design, producer patterns, and failure modes; participate in on-call for the platform you own.

- Mentor engineers on streaming architecture and raise the team's bar through design review and documentation.

📌 Senior Data Platform Engineer (India)
🏢 Mirantis
📍 India