The opportunity
The Product Solutions Architecture (PSA) team acts as a technical multiplier across Datadog. PSAs are domain experts who partner with Field teams on complex customer use cases across pre- and post-sales engagements and scale their impact by producing reusable collateral,…
What you'll do
Serve as the in-house subject matter expert for Datadog's Cloud Cost: Management products, including cost visibility across AWS, Azure, and GCP, Kubernetes and container cost allocation, custom and SaaS cost ingestion, budgets, anomaly detection, and cost optimization recommendations
Provide architectural guidance on capacity planning, comparing cloud service: cost models, and balancing performance vs. cost trade-offs across different cloud environments
Guide customers on tokenomics and AI cost attribution across providers: (Anthropic, OpenAI, AWS Bedrock, Google Gemini, Vertex AI, and GitHub Copilot), helping teams map API keys, models, and usage to teams and business units using Tag Pipelines.
Help enterprise clients track real-time AI spend changes, set up cost anomaly: monitoring, and connect token usage back to business outcomes and unit economics (such as cost per user or cost per ticket).
Partner with Field teams to provide hands-on technical and architectural: guidance to enterprise customers adopting CCM, including designing tagging and cost allocation strategies, showback and chargeback models, and cost reporting that connects spend to services, teams, and business units
Create high-impact technical collateral, including reference architectures,: technical guides, blog articles, and documentation to enable Field teams and the broader customer community, covering topics like cloud billing data onboarding (AWS, Azure, GCP billing), Kubernetes cost attribution, token usage, unit economics, and rightsizing and commitment optimization
What they're looking for
- You bring a strong engineering foundation, with hands-on experience managing,: allocating, or optimizing cloud spend, or building FinOps programs and internal cost tooling in production environments at a large scale, and are comfortable diving deep into billing data and architectural tradeoffs to understand what drives cost
- You are familiar with cloud infrastructure and core observability concepts,: such as tracing, metrics, and logging, and understand how compute, storage, networking, and container platforms (including Kubernetes) translate into cloud bills
- Deep understanding of cost models, performance characteristics, and capacity: planning across various cloud services and managed platforms
- Experience with languages such as Python and/or SQL, and familiarity with: querying and modeling large billing datasets, tagging strategies, and infrastructure-as-code
- Comfortable operating in rapidly evolving, ambiguous technical domains
- You build deep context across teams and translate it into reusable, scalable solutions
- You take ownership from problem definition through implementation and measurable outcomes
- Highly detail-oriented, particularly when working on architectures and customer-facing technical assets
