Senior Software Engineer, Data Infrastructure

Roblox•San Mateo, CA

11h•$243,290 - $295,250•Hybrid

About The Position

Roblox's data infrastructure processes petabytes of data daily, powering analytics, ML, and product decisions. As a Senior Software Engineer in our Data Infra org, you will design, build, and scale the distributed data infrastructure platforms that power Roblox. You will own and drive the next-generation architecture of our core platforms, which span Kafka, Flink, Spark, Trino, Druid, Airflow and Data Catalog. This role combines high ambiguity and ownership to push the boundaries of what our infrastructure can handle at massive scale, giving you the unique opportunity to steer the evolution of the data landscape.

Requirements

5+ years of experience building, designing, testing and maintaining production-grade, large-scale distributed systems.
Data Platform Depth: Deep technical experience building or strong familiarity with at least 1 or 2 foundational technologies within our stack: Kafka, Flink, Spark, Trino, Druid, Airflow, or Data Catalog/Metadata systems.
Strong Engineering Foundations: Robust proficiency in Java, Go, or Scala, with a track record of writing clean, highly performant backend code.
Cloud Fluency: Experience operating and troubleshooting data infrastructure at scale on top of Kubernetes in AWS or GCP.
Technical Leadership: Demonstrated ability to influence technical direction across teams, mentor engineers, and drive alignment on complex cross-cutting initiatives.
B.S. equivalent in CS or sufficient experience.

Nice To Haves

Contributions to open-source projects in the data infrastructure ecosystem.
Experience operating infrastructure at consumer-internet scale (100M+ users).

Responsibilities

Own and Scale Core Platform Components: Take responsibility for the design, architecture, and implementation of 1–2 key data platform frameworks within our stack
Collaborate and Align: Partner with infra, data science, and product engineering teams to ensure your target platform's capabilities are directly guided by platform governance and product requirements.
Optimize Performance at Scale: Dive deep into engine internals, query planning, state management, memory optimization, serialization efficiency to maximize throughput and reliability under heavy load.
Drive Infrastructure Robustness: Lead the design, testing, and operational lifecycle of next-generation infrastructure features running on Kubernetes across cloud environments.
Agentic Interface: Embed AI/ML capabilities within our data platforms, leveraging LLMs for data discovery and generation, building autonomous, self-serve mechanisms for platform interaction layer.