Datadog
Product Solutions Architect - FinOps
$152K–$202K
Boston, Massachusetts, USA +9 · Full-time · Posted Oct 8, 2026
- Location
- Boston, Massachusetts, USA
Chicago, Illinois, USA
Denver, Colorado, USA
New York, New York, USA
San Francisco, California, USA
Boston, MA, United States
Chicago, Illinois, United States
Denver, Colorado, United States
New York, NY, United States
San Francisco, CA, United States
About the job
Partner with customers, Field teams, and Product Management to design, implement, and expand adoption of Datadog Cloud Cost Management, including cloud and AI cost attribution and optimization.
What you'll do
- Provide architectural and hands-on customer guidance on cloud cost allocation, reporting, capacity planning, and optimization.
- Advise customers on AI token cost attribution, anomaly monitoring, and connecting usage to business outcomes.
- Create reusable technical collateral and relay customer insights to Product Management to inform the product roadmap.
What they ask for
- Hands-on experience managing, allocating, or optimizing cloud spend, building FinOps programs, or developing internal cost tooling in production at large scale.
- Familiarity with cloud infrastructure and observability concepts, including tracing, metrics, logging, and Kubernetes.
- Deep understanding of cloud service cost models, performance, and capacity planning.
- Experience with Python and/or SQL, and familiarity with large billing datasets, tagging strategies, and infrastructure-as-code.
Nice to have
- 5+ years as a Solutions Architect, Sales Engineer, or FinOps practitioner supporting cloud cost management, FinOps, or cloud financial optimization tools.
- Experience with Datadog or other observability tools; familiarity with FinOps Foundation practices, commitment management, or FinOps certification.
Pay
$152,000 — $202,000 USD yearly salary (base salary)
Tools
AWS, Azure, GCP, Kubernetes, Python, SQL, Anthropic, OpenAI
AI focus
Guide customers on tokenomics and AI cost attribution across providers; help track AI spend, monitor cost anomalies, and connect token usage to business outcomes. The posting also asks for capability in utilizing and scaling AI tooling.
Model-written summary · the original posting is authoritative