Software Engineer

Husky is what we call the distributed, petabyte-scale columnar event store at the heart of our Event Platform, which powers dozens of Datadog’s most popular products – Logs, RUM, APM, Cloud Network Monitoring, Netflow, and many more. Husky was built from the ground up at Datadog to store and query massive volumes of event data at low cost, with data fully queryable within seconds of arrival.

As a Senior Software Engineer on this team, you'll work hands-on with a distributed storage system at petabyte scale, and help us evolve our platform to support the next level of scale that will power the AI era. We will move from a custom storage format towards open standards (Parquet, Arrow, DataFusion) enabling on prem solutions, increasingly complex analytical/batch workloads, and powering AI observability, sustainably into the future. You'll partner closely with Husky Ingestion, Metadata, and Platform, and increasingly with Lakehouse on the Iceberg-compatible boundary between Husky and analytical/batch workloads. You will also work with the open source community to  build the next generation of reusable software solutions that can be operated at scale.

Datadog has a culture that is increasingly rare in mature technology companies. We are an engineering driven, bottom up, high agency/high responsibility culture–where everyone is empowered and expected to drive the technological and strategic direction of the business over time. We are a blameless culture where we believe supporting each other through challenges produces the best outcomes over time and our track record corroborates this. We are open source champions. We believe in the flexibility of working remotely but also the value of bringing people together across our various offices worldwide–and our offices are nice places to work.

What You’ll Do: 

• Drive Husky to the next level of adoption by migrating our storage format to open source Parquet and re-imagine compaction on a format-agnostic, DataFusion-based substrate
• Drive concrete cost-reduction work: block-level inverted index compression, combining the database with external objects at the row level, and storage tiering/caching
• Scale our solution to enable customers to manage event versioning at scale, using temporal-database techniques.
• Play an influential role in operating and deciding how to best operate our service going forward.
• Work across the write path (with Ingestion) and toward a cleaner Iceberg-based handoff to ADP/Lakehouse for batch/analytical workloads