Principal Cloud Architect

About the role

Prototype and hand off platform patterns end-to-end—build reference implementations, document the rationale, and partner with implementation teams to roll them out. Stay hands-on through proof-of-concept and initial enablement, writing reference implementations and opening PRs to jumpstart team adoption.

Participate in Architecture Review: bring a consistent, documented rubric to weekly architecture reviews so teams get reliable guidance. Expand our standards library and Architecture Decision Records (ADRs).

Own a Domain Specialty: provide architectural stewardship in one of our focus areas:

• Cloud Infrastructure Architecture: own the architecture for our cloud runtime, networking, service mesh, container platforms, and overall infrastructure scalability, resilience, and security.
• Data Architecture: own event streaming/messaging, database technology strategy, pipeline patterns, warehouse, and lakehouse strategies.

Define Technology Lifecycle: evaluate emerging technologies through structured POCs, drive standards for onboarding and phase-out, and lead evaluations of vendor changes and consolidation opportunities. Partner with FinOps on cost optimization in your depth area, including emerging AI/model infrastructure spend (e.g., evaluating open-weight models as a lower-cost alternative to hosted APIs).

Enable R&D Teams: host design reviews and workshops. Serve as a technical consultant to engineering teams making complex infrastructure choices. Make patterns consumable and self-service so teams don’t reinvent the wheel.

Shape Multi-Year Strategy: contribute to the multi-year platform direction—developer experience, delivery pipelines, and AI-augmented architecture review tooling (e.g., MCP servers, LLM-based rubric evaluation)—in partnership with platform engineering teams.

Requirements

• Broad Infrastructure Architecture Experience: proven track record of setting technical direction across large-scale systems many teams depend on—cloud, networking, data, or delivery. Ability to defend architectural choices against real production workloads.
• Multi-Cloud & Kubernetes Fluency: production experience with GCP and/or AWS, paired with solid understanding of trade-offs. Practical experience with Kubernetes (GKE or equivalent) as the runtime foundation for modern workloads.
• Infrastructure as Code: strong proficiency in Terraform, GitOps workflows, and designing/reviewing reusable modules that other teams depend on.
• Depth in At Least One Domain (nice to have more):
  • Cloud Infrastructure: cloud-native networking (AWS transit patterns, GCP shared VPCs, Private Service Connect, VPC peering); Kubernetes/container orchestration (GKE or equivalent) and service mesh (Istio) and mTLS; designing for scalability, resilience, and cost efficiency; ensuring infrastructure and network security.
  • Data Architecture: messaging platforms (Kafka/Confluent, Pub/Sub); database technology fluency across SQL (PostgreSQL, Cloud SQL), NoSQL (MongoDB), columnar analytics (Snowflake, BigQuery), and search (Solr, Elasticsearch); pipeline design (Dataflow, Cloud Composer, MSK, Kinesis); open table formats (Apache Iceberg).
• Development & Operational Depth: ability to dive into code to prove out patterns and evaluate system behavior under load (incidents, on-call impact, capacity, rollout blast radius). Comfortable shipping working prototypes rather than relying solely on high-level diagrams.
• Influence & Leadership: strong technical leadership, writing, and presentation skills. Track record of driving standards adoption across engineering organizations without formal managerial authority—through documented rationale, prototypes, workshops, and review processes.
• Technology Evaluation: demonstrated ability to run rigorous technology evaluations—separating vendor pitch from architectural fit, running POCs, and producing actionable recommendations for engineering leaders.
• AI Infrastructure Fluency: working knowledge of the infrastructure demands of AI/ML workloads (model serving, vector databases, inference cost and latency trade-offs); able to weigh in on where AI fits into the platform’s technical roadmap.

Nice to have

• Depth in both Cloud Infrastructure and Data Infrastructure domains.
• Experience with GCP organization-level policies, folder structure, and IAM inheritance design.
• Confluent Platform knowledge beyond core Kafka (Schema Registry, Kafka Connect, ksqlDB).
• Experience running or participating in an Architecture Review Council or equivalent governance body.
• Hands-on experience building or applying AI-augmented architecture review workflows (e.g., MCP servers, LLM-based rubric evaluation).
• MLOps tooling experience (e.g., MLflow, Kubeflow, Vertex AI Pipelines, SageMaker).

Qualifications

Bachelor's degree in Computer Science, related technical field, or equivalent practical experience. 10-15+ years of cloud infrastructure, platform, DevOps, or data architecture experience.

Pay

US base salary range: $157,500–$247,500 USD. Additional compensation such as bonus, commission, equity, and other benefits may also apply.

Benefits

Comprehensive benefits including holistic mind, body, and lifestyle programs designed for overall well-being. Learn more about ZoomInfo benefits here.