Software Engineer, Data

LiveKit

About The Position

We're looking for a Software Engineer, Data to help design and operate the metering and analytics infrastructure that powers LiveKit's platform. Our systems process massive volumes of usage data generated by thousands of developers and their end-users across billions of sessions, spanning real-time analytics, long-term trend analysis, data transfer, data governance, and retention policies. The infrastructure is heavily built on Go, with data flowing through blob stores, SQL stores, and ClickHouse across dozens of global regions. This is a deeply socio-technical role - you'll work closely with engineers across every team to ensure correctness of metering and analytics for their domains, while collaborating on practical designs that hold up in production at scale. If you're energized by building resilient data infrastructure and have strong opinions about schema evolution and data quality, this is the role.

Requirements

You have strong experience designing and operating data pipelines and distributed systems in production across dozens of global regions
You're a Gopher who has worked extensively with Go and contributes comfortably to a distributed systems architecture
You have deep experience with columnar/analytical databases (ClickHouse, BigQuery, or similar) and blob storage for high-volume workloads
You think deeply about data correctness - delivery semantics, idempotency, schema compatibility, and the failure modes that cause silent data loss
You're a strong cross-team collaborator who translates domain requirements into practical infrastructure designs
You have previous experience working on data-intensive SaaS applications with web-based dashboards in the analytics (reporting, observability or finance) space

Nice To Haves

Experience with stream processing frameworks (Kafka, Pulsar)
Kubernetes
OpenTelemetry
query federation engines (Trino, Presto, Dremio)
protobuf/Avro schema registries
usage-based billing/metering systems

Responsibilities

Design and evolve metering and analytics infrastructure spanning real-time analytics, long-term analysis, data transfer, governance, and retention policies
Collaborate with teams across the organization to ensure metering and analytics are correct and complete for their domains - Agents, Agent Insights, Cloud Dashboard, and customer-facing reporting
Monitor and manage datasets with varying cardinality - both internally-defined datasets we control and customer-produced datasets where cardinality is unbounded and efficient querying is essential
Ensure data reliability through delivery guarantees, dead letter queues, reconciliation, validation, alerting, and anomaly detection across our distributed service fleet
Design and enforce schema evolution strategies (e.g., schema registries, backward/forward compatibility contracts) to evolve infrastructure without breaking downstream consumers
Optimize ClickHouse and blob storage for query performance, cost efficiency, and reliability across global regions
Reduce operational toil through automation, self-service tooling, and runbooks