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Data Infrastructure Engineer

Salary not listed • Stockholm • Full-time

Posted 23d ago

Remote Work Policy

On-site

Employment Type

Full-time

Categories

AI Infrastructure Engineer

About the job

Lovable is seeking an infrastructure engineer to build the data ingestion, storage, and query systems that power their agentic software creation platform. This role is crucial for ensuring the reliability of data for applications built on Lovable. The company is a fast-growing AI company based in Stockholm, with a culture of ownership, velocity, and collaboration. They are looking for individuals who are passionate about making a significant impact in the next era of software development.

Responsibilities

  • Build ingestion systems that accept events from millions of applications, treating each producer as untrusted and preventing tenant data corruption or slowdowns.
  • Develop managed analytical storage solutions for each user, including schema management, partitioning, and compaction for continuous small writes.
  • Optimize query performance without maintaining warm compute for every tenant.
  • Construct the runtime for agent-written connectors, ensuring isolated execution, credential management, scheduling, retries, and state handling.
  • Measure and reduce cost per tenant through metering, limits, and workload isolation.
  • Expose data, metadata, and tools to the Lovable agent for building software on customer data.
  • Operate and maintain built systems, including observability, data freshness, and recovery processes.

Requirements

  • Experience building and operating large-scale production data systems, specifically with the challenge of millions of tiny tenants on shared infrastructure.
  • Deep expertise in at least one data layer (ingestion, storage, query execution, or orchestration) and the ability to work across multiple layers.
  • Experience with multi-tenant or untrusted workloads, including isolation, managing noisy neighbors, and maintaining low cost per tenant.
  • Strong judgment for making critical design decisions, including abstraction choices, cost vs. latency tradeoffs, and scalability assessments.
  • Proven ability to ship and own production systems.
  • Familiarity with AI agents and building systems that support them.