Requirements:

You have 6+ years of experience as a data or platform engineer, including designing large-scale distributed systems for ML or analytics workloads.
You’ve built and operated data pipelines or storage systems with technologies like Kafka, Snowflake, ClickHouse, dbt, or similar.
You are proficient in Python, Scala, Go, or Java, and have production experience working in cloud environments (e.g., AWS).
You understand data model design and storage formats optimized for ML workflows (e.g., columnar formats, LSM trees).
You value cross-functional collaboration and can work closely with applied scientists, backend engineers, and infra teams.

Nice to Haves:

Bonus: experience building or operating a feature store, embedding database, or metadata management platform.

Design and build distributed feature pipelines that power both training and low-latency inference use cases.
Implement feature versioning, cataloging, and access management capabilities, ensuring traceability and governance across teams.
Drive the integration of embedding storage systems (e.g., DDVector) to support scalable training and real-time retrieval.
Build tooling to surface feature metadata, dependencies, and usage analytics to empower discovery and reuse.
Partner with training, serving, and registry teams to deliver seamless integration across the AI Platform lifecycle.
Own the operational excellence of your systems through monitoring, performance optimization, and cost efficiency.

New hire stock equity (RSUs) and employee stock purchase plan (ESPP)
Continuous professional development, product training, and career pathing
Intradepartmental mentor and buddy program for in-house networking
An inclusive company culture, ability to join our Community Guilds (Datadog employee resource groups)
Access to Inclusion Talks, our internal panel discussions
Free, global mental health benefits for employees and dependents age 6+
Competitive global benefits