About this role
Headquarters: Remote-US-NY
- 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.
- 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.
- 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).
- 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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