USAPrincipal Cloud Architect
3 нед. назад
USALeadRemote
cloud infrastructureservice meshcontainer platformsdatabase technologyevent streamingmessagingdata pipeline
Lead cloud infrastructure and data architecture design, guiding implementation teams and setting architectural standards.
Обязанности
- Prototype and Hand Off: Design 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.
Требования
- 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.
Будет плюсом
- 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).
Другое
- is where careers accelerate. We move fast, think boldly, and empower you to do the best work of your life. You’ll be surrounded by teammates who care deeply, challenge each other, and celebrate wins. With tools that amplify your impact and a culture that backs your ambition, you won’t just contribute. You’ll make things happen–fast.
- 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.
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- Actual compensation offered will be based on factors such as the candidate’s work location, qualifications, skills, experience and/or training. Your recruiter can share more information about the specific salary range for your desired work location during the hiring process. We want our employees and their families to thrive.
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- About us:
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