Requirements:

You have 3+ years of experience as a software, data or ML engineer, working on distributed systems or ML data pipelines.
You’ve built and maintained data systems using technologies like Kafka, Spark.
You have experience with postgres and other databases technologies.
You’re fluent in Python, Go, Scala, or Java and have experience working in cloud-native environments.
You have a strong understanding of data modeling and storage formats optimized for machine learning.

Nice to Haves:

Build and maintain distributed data pipelines that power feature engineering for training and inference.
Ensure reliability and scalability of data stores for both classic features and embeddings.
Contribute to the design of feature cataloging, lineage, and access control systems across teams and regions.
Integrate embedding stores and real-time serving backends to support low-latency inference use cases.
Build internal tooling to improve feature discoverability, observability, and governance.
Collaborate with ML engineers, backend developers, and infra teams to ensure end-to-end feature platform cohesion.

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