Senior Software Engineer, Roblox

• 6+ years of experience building, designing, testing and maintaining production-grade, large-scale distributed systems
• Deep OLAP & MPP Engine Depth: Strong hands-on engineering or operational experience with StarRocks or adjacent OLAP engines such as Apache Druid, ClickHouse, Apache Doris, Apache Pinot, or Trino/Presto
• Streaming & Ingestion Architecture: Proven track record implementing high-volume streaming ingestion using Apache Kafka, Apache Flink, Routine Load / Broker Load, or Debezium/CDC pipelines
• Data Modeling & Query Optimization: Expertise in OLAP schema design (partitioning, bucketing, sort keys, indexing) and tuning cost-based SQL query engines for massive scale
• Strong Engineering Foundations: Robust proficiency in Java, Go, C++, or Scala, with a track record of writing clean, highly performant distributed backend code
• Infrastructure & Orchestration: Experience operating, troubleshooting, and provisioning data infrastructure at scale using Kubernetes and Terraform on 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
• For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time

Desirable

• Roblox's data infrastructure processes petabytes of data daily, powering analytics, ML, and product decisions for a platform serving 200M+ daily active users
• As a Senior Software Engineer on our Real-time Analytics Platform team, you will design, build, and scale the core OLAP infrastructure powering sub-second analytical queries across Roblox
• Our team directly owns and operates StarRocks and Apache Druid, driving high-throughput streaming ingestion and low-latency MPP query processing
• You will lead the next-generation architecture of our real-time analytics platform, combining high ownership and deep technical complexity to push the boundaries of MPP engine performance, streaming ingestion pipelines, and cluster scalability
• Own and Scale Core OLAP Platforms: Take end-to-end ownership of the architecture, operational health, and scalability of our high-throughput OLAP engines, specifically StarRocks and Apache Druid
• Optimize Query Engine Performance: Dive deep into engine internals, cost-based optimization (CBO), query planning, vectorized execution, memory optimization, and serialization efficiency to maximize throughput and maintain sub-second latency under heavy concurrent load
• Architect Streaming Ingestion Pipelines: Design, build, and optimize real-time data ingestion paths leveraging Apache Kafka, Apache Flink and native engine load mechanisms
• Drive OLAP Data Modeling Best Practices: Partner with infra, data science, and product engineering teams to guide data modeling for MPP databases, optimizing partitioning, bucketing, sort keys, and materialized views
• Manage Cluster Infrastructure & Capacity: Lead cluster provisioning, lifecycle management, operations, and capacity planning using Kubernetes and Terraform across cloud environments
• Advance Autonomous Interfaces: Integrate AI/ML and LLM-driven capabilities within our query platforms to enable automated self-serve mechanisms and intelligent data discovery for engineers