About the role
TL;DR: Build the data infrastructure the Lovable agent runs on: ingestion, storage and query for millions of small tenants instead of one large one. You'll join the Data Platform team, and your work decides whether the apps people build on Lovable can rely on their data.
What we’re looking for
An infrastructure engineer who has built and run data systems in production at scale, and wants the harder version of the problem: millions of tiny tenants on shared infrastructure.
Deep experience in at least one layer (ingestion, storage formats and table layout, query execution, or orchestration), and the range to work across the others.
Experience with multi-tenant or untrusted workloads: isolation, noisy neighbours, and keeping cost per tenant low.
Good judgment on calls no one can check for you: the right abstraction, the tradeoff between cost and latency, whether a design holds at 10x.
You work with AI agents end to end. Your output is the systems you build, not the code you type by hand.
You ship, and you own what you ship in production.
What you’ll do
Build ingestion that accepts events from millions of Lovable apps, treats every producer as untrusted, and keeps one tenant's bad traffic from slowing or corrupting another's data.
Build managed analytical storage for every user, including schema management, partitioning, and compaction of a constant stream of small writes.
Make queries fast when you can't keep compute warm for every tenant.
Build the runtime for agent-written connectors: isolated execution, credentials, scheduling, retries and state.
Make cost per tenant measurable, then drive it down, through metering, limits and workload isolation.
Expose data, metadata and tools to the Lovable agent so it can build software on customers' data.
Run what you build, including observability, freshness and recovery.
The tech stack
We're building with tools that both humans and AI love:
Languages: Go, Rust, Python, SQL
Data: ClickHouse, BigQuery, Parquet on object storage
Streaming & orchestration: Pub/Sub, Temporal
Cloud: GCP, Cloudflare, AWS
DevOps & tooling: GitHub Actions, Grafana, OTEL, Terraform
And always on the lookout for what's next.