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Staff Engineer, Compute
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At a glance
- Employment type
- Full-time
- Workplace
- Hybrid
- Category
- Full-stack
Technologies & skills
What you'll be doing
Lead technical direction for Datadog's Kubernetes platform, focusing on capacity management and workload placement across AWS, GCP, and Azure. Design and build systems for scheduling and deploying workloads at scale, influence platform architecture, and solve infrastructure challenges in a highly technical, high-impact role.
- Lead technical direction of capacity management and workload placement for Kubernetes platform
- Design and build systems optimizing workload scheduling and deployment across regions
- Partner with infrastructure teams to evolve multi-region and multi-cloud capacity orchestration
- Develop production software in Go to improve Kubernetes platform capabilities and automation
- Use data and capacity signals to influence infrastructure decisions and improve workload placement strategies
Key requirements
Must-have
- Significant experience designing and operating large-scale Kubernetes-based infrastructure or platform systems
- Strong programming skills, ideally in Go or a comparable systems programming language
- Hands-on experience with at least one major cloud provider (AWS, Google Cloud, or Azure)
- Strong systems mindset and experience solving infrastructure, scheduling, or capacity management challenges
- Comfort working with operational data, capacity forecasting, or analytical approaches
- Proven track record of providing technical leadership and influencing architecture
Role signals
- Technical focus
- infrastructure, capacity management, workload placement, platform architecture
- Leadership
- Mentoring
- Architecture / system design
- Indicated in the listing
- Hands-on vs management
- Hands-on
Similar jobs
Full job description
As a Staff Engineer on Datadog's Compute – Disruption and Workload Placement team, you'll help define how our Kubernetes fleet scales to meet the demands of rapidly growing AI and cloud-native workloads. You'll work on the systems that ensure engineering teams have the right compute capacity, in the right region, at the right time across AWS, Google Cloud, and Azure. This is a highly technical, high-impact role where you'll shape the future of capacity orchestration, influence platform architecture, and solve infrastructure challenges that directly support Datadog's continued growth.
At Datadog, we place value in our office culture - the relationships and collaboration it builds and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them.
What You'll Do
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Lead the technical direction of capacity management and workload placement for Datadog's Kubernetes platform spanning 100,000+ virtual machines across multiple cloud providers.
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Design and build systems that optimize how engineering workloads are scheduled and deployed across regions while balancing capacity constraints, reliability, and performance.
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Partner across infrastructure teams to evolve multi-region and multi-cloud capacity orchestration as Datadog continues to scale.
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Develop production software in Go to improve Kubernetes platform capabilities, automation, and operational efficiency.
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Use data and capacity signals to influence infrastructure decisions, forecast growth, and improve workload placement strategies.
Who You Are
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You have significant experience designing and operating large-scale Kubernetes-based infrastructure or platform systems.
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You are an experienced software engineer with strong programming skills, ideally in Go or a comparable systems programming language.
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You have hands-on experience with at least one major cloud provider (AWS, Google Cloud, or Azure) and understand distributed cloud infrastructure.
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You have a strong systems mindset and enjoy solving complex infrastructure, scheduling, or capacity management challenges.
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You are comfortable working with operational data, capacity forecasting, or analytical approaches that inform engineering decisions.
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You have a proven track record of providing technical leadership across teams and influencing architecture without relying on direct management authority.
Datadog values people from all walks of life. We know not everyone will meet all the above qualifications on day one. That’s okay. If you’re passionate about technology and want to grow your experience, we encourage you to apply.
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Benefits and Growth:
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Generous and competitive benefits package
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New hire stock equity (RSUs) and employee stock purchase plan
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Continuous career development and pathing opportunities
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Employee-focused best in class onboarding
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Internal mentor and cross-departmental buddy program
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Friendly and inclusive workplace culture
Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog.
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**About Datadog: **
Datadog is the leading observability and security platform for the AI era, providing businesses with unified visibility across the technology stack to manage complexity at scale. It brings applications, infrastructure, data, models, and security into one place, using AI to detect and resolve issues before they impact customers. Trusted globally by Fortune 500 companies and high-growth AI leaders, Datadog enables businesses to move faster with clarity and confidence. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.
- Equal Opportunity at Datadog:
Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.
Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications.
- Privacy and AI Guidelines:
Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
You'll be assessed on your expertise in large-scale Kubernetes infrastructure, multi-cloud capacity management, Go programming, and technical leadership. Expect focus on designing scalable systems for workload placement, optimizing compute resources, and influencing platform architecture across AWS, GCP, and Azure.
- Kubernetes scaling·High
- Multi-cloud orchestration·High
- Go development·High
- Capacity management·High
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Large-Scale Kubernetes Projects
0–10 minList and summarize your most relevant Kubernetes infrastructure experiences, focusing on scale, challenges, and solutions.
Refresh Go and Automation Skills
10–17 minSkim recent Go codebases or notes, especially those related to infrastructure automation and Kubernetes integration.
Prepare Leadership and Collaboration Stories
17–24 minRecall examples where you influenced architecture or led cross-team initiatives, noting outcomes and lessons.
Research Datadog’s Compute Platform
24–30 minRead about Datadog’s compute and observability offerings to align your experience with their mission and challenges.
Talking points
, 6 itemsKubernetes Infrastructure at Scale
You will lead efforts to manage and scale a Kubernetes fleet spanning 100,000+ VMs, requiring deep understanding of cluster operations and workload scheduling.
Multi-Cloud Capacity Orchestration
The role demands designing solutions that work seamlessly across AWS, GCP, and Azure, balancing capacity, reliability, and performance.
Go Programming for Platform Engineering
You will develop production software in Go to enhance Kubernetes platform capabilities and automation.
Capacity Forecasting and Operational Data Analysis
Using data to inform infrastructure decisions and forecast growth is central to optimizing workload placement and resource allocation.
Technical Leadership and Architectural Influence
You are expected to provide technical direction and influence architecture across teams without direct management authority.
Workload Placement Strategies
Optimizing how engineering workloads are scheduled and deployed across regions is a core responsibility.
What to research
, 2 itemsKubernetes Scaling and Workload Placement
Expect deep questions on your ability to design, operate, and optimize large Kubernetes fleets, especially for AI and cloud-native workloads.
Technical Leadership Across Teams
Prepare to demonstrate how you have influenced architecture and provided technical direction without direct management authority.
Questions to ask
, 6 itemsHow does the team prioritize between reliability, performance, and cost when managing compute capacity?
Why ask this? Clarifies decision-making criteria and expectations for balancing trade-offs.
What are the biggest technical challenges the Compute – Disruption and Workload Placement team is currently facing?
Why ask this? Reveals current pain points and where your expertise could add value.
How is success measured for this role and team?
Why ask this? Helps you understand performance expectations and key metrics.
What is the typical process for cross-team collaboration on infrastructure projects?
Why ask this? Provides insight into communication and partnership norms.
How does Datadog support continuous learning and technical growth for staff engineers?
Why ask this? Shows the company’s commitment to your ongoing development.
What tools and frameworks are most commonly used for capacity forecasting and operational data analysis?
Why ask this? Clarifies the technical stack and where you may need to ramp up.
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