Job description

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators. At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there. A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. Roblox’s data platform powers analytics, ML, and product decisions for a platform serving 200M+ daily active users. As a Principal Software Engineer in the Data Platform Orchestration team, you will be the technical leader for the system that schedules and runs the company’s data workloads. Our team owns Apache Airflow and the Kubernetes (EKS) infrastructure underneath it, running 8,000+ DAGs every day, most of them Spark jobs. You will set the long-term architecture for how Roblox orchestrates data at scale, and you will be hands-on in making it faster, more reliable, and easier for hundreds of engineers to use. You Will: Define the Orchestration Strategy: Own the multi-year architecture for Airflow and the surrounding platform, including scheduler and executor design, multi-tenancy, upgrades and migrations, and where the platform should evolve next. Scale and Harden Airflow: Find and remove scaling limits across the scheduler, metadata database, executors, and DAG parsing. Make sure thousands of daily DAGs run with predictable latency and strong SLAs. Own Orchestration on Kubernetes: Lead the design and operation of our EKS clusters for orchestration and Spark workloads, including autoscaling, resource isolation, cost efficiency, and deployment automation. Build Platform Tooling for Internal Engineers: Design the APIs, custom operators and providers, libraries, and self-service tooling that let data teams build, deploy, and monitor pipelines safely. Drive Observability and Reliability: Establish SLOs, alerting, and diagnostics for pipelines and platform components. Lead incident reviews and fix the root causes in code, not just in runbooks. Lead Across Teams: Work with data platform, governance, data science, and product engineering teams to align roadmaps, guide technical decisions, and build consensus on complex initiatives. Raise the Engineering Bar: Set standards for code quality, design, and operational readiness. Mentor staff, senior, and mid-level engineers. You Have: B.S. equivalent in CS or sufficient experience. 10+ years of experience designing, building, and operating production-grade, large-scale distributed systems. Workflow Orchestration Depth: Deep, hands-on experience designing, running, and scaling a production data-related workflow orchestrator or job scheduler (thousands of workflows or DAGs a day). Apache Airflow is ideal, but experience with an equivalent system (Dagster, Prefect, Temporal, or a large in-house scheduler) is also welcome. You understand scheduler and executor design, metadata store performance, task isolation, retries and idempotency, and safe upgrades and migrations. Kubernetes Expertise: Proven experience managing and scaling production Kubernetes clusters on AWS (EKS), including Helm, autoscaling (Karpenter or Cluster Autoscaler), and GitOps-style deployment. Software Engineering Foundations: Strong proficiency in Python, plus Go or Java, with a record of writing clean, well-tested platform and tooling code. You are a software engineer who builds and improves systems, not someone who mainly handles tickets and operations. Data Workload Familiarity: Working knowledge of how Spark jobs run on Kubernetes (Spark on K8s, spark-operator, or EMR on EKS) and how orchestration interacts with them. Platform Mindset: Experience building platforms used by other engineers, with strong instincts for APIs, observability, reliability, and developer experience. Cross-Organizational Technical Leadership: A track record of influencing direction across a large engineering organization and driving multi-quarter projects from concept to production. Extreme Ownership: You define the problem when requirements are unclear, unblock dependencies, and see complex work through to stable production. Bonus Points: Contributions to Apache Airflow or related open-source projects (Airflow providers, Helm chart, spark-operator, Karpenter, Argo CD). Experience with other orchestration systems (Dagster, Temporal, Argo Workflows, Flyte) and the judgment to know when each fits. Experience with Terraform or other infrastructure-as-code, Prometheus/OpenTelemetry, and cost optimization or chargeback for shared platforms. Experience operating infrastructure at consumer-internet scale. 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 $295,250 — $345,040 USD Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted). Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process. 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.

Requirements

Must-haves

Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.
Scale and Harden Airflow: Find and remove scaling limits across the scheduler, metadata database, executors, and DAG parsing. Make sure thousands of daily DAGs run with predictable latency and strong SLAs.
Own Orchestration on Kubernetes: Lead the design and operation of our EKS clusters for orchestration and Spark workloads, including autoscaling, resource isolation, cost efficiency, and deployment automation.
Build Platform Tooling for Internal Engineers: Design the APIs, custom operators and providers, libraries, and self-service tooling that let data teams build, deploy, and monitor pipelines safely.
Drive Observability and Reliability: Establish SLOs, alerting, and diagnostics for pipelines and platform components. Lead incident reviews and fix the root causes in code, not just in runbooks.
Lead Across Teams: Work with data platform, governance, data science, and product engineering teams to align roadmaps, guide technical decisions, and build consensus on complex initiatives.
Raise the Engineering Bar: Set standards for code quality, design, and operational readiness. Mentor staff, senior, and mid-level engineers.
B.S. equivalent in CS or sufficient experience.
10+ years of experience designing, building, and operating production-grade, large-scale distributed systems.
Kubernetes Expertise: Proven experience managing and scaling production Kubernetes clusters on AWS (EKS), including Helm, autoscaling (Karpenter or Cluster Autoscaler), and GitOps-style deployment.
Data Workload Familiarity: Working knowledge of how Spark jobs run on Kubernetes (Spark on K8s, spark-operator, or EMR on EKS) and how orchestration interacts with them.
Platform Mindset: Experience building platforms used by other engineers, with strong instincts for APIs, observability, reliability, and developer experience.
Cross-Organizational Technical Leadership: A track record of influencing direction across a large engineering organization and driving multi-quarter projects from concept to production.
Extreme Ownership: You define the problem when requirements are unclear, unblock dependencies, and see complex work through to stable production.

Nice-to-haves

Contributions to Apache Airflow or related open-source projects (Airflow providers, Helm chart, spark-operator, Karpenter, Argo CD).
Experience with other orchestration systems (Dagster, Temporal, Argo Workflows, Flyte) and the judgment to know when each fits.
Experience with Terraform or other infrastructure-as-code, Prometheus/OpenTelemetry, and cost optimization or chargeback for shared platforms.
Experience operating infrastructure at consumer-internet scale.

Application prep

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Keywords to mirror

mlkubernetesawspythongojava

Platform: greenhouse
Effort: quick

Benefits

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