Your Role: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.
You Will:- 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.
You Have:- 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.
- Bonus Points:
- Core contributions to open-source OLAP databases (StarRocks, Apache Druid, ClickHouse, Apache Doris).
- Experience operating real-time analytical platforms at consumer-internet scale (100M+ active users).
For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on
this page.
Annual Salary Range
$243,290-$295,250 USD
Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).
