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. As a Principal Infrastructure Architect on our Infrastructure Architecture team, you will be the architecture team's partner to ZoomInfo's data engineering organization. Data engineering owns the platforms and the products built on them. You bring the infrastructure patterns, evaluation rigor, and cross-team alignment that help them build and run those platforms well on GCP. This is a cloud architect seat with a data specialty. You bring the same cloud, Kubernetes, networking, and infrastructure-as-code foundation as the rest of the architecture team, plus enough hands-on depth in data systems to be credible with the engineers who run them. You establish and maintain the paved-road patterns data teams build on: infrastructure as code, CI/CD for data jobs and pipelines, monitoring and alerting, performance, and cost. You keep them easy to consume and self-service. You help data teams adopt those patterns, review their designs, and work through hard infrastructure trade-offs with them. You run POCs of current data technologies and vendor offerings as part of the procurement and technology-lifecycle process, and you write recommendations engineering leaders can act on. You take a full share of the architecture team's general work: weekly architecture reviews across all domains, standards and ADRs, and infrastructure consulting for any team that needs it. The data estate is broad. You'll write reference implementations, open the first PRs, and stay hands-on through enablement when a critical initiative calls for it. Establish infrastructure patterns for data workloads.



Design and maintain reference patterns for provisioning and operating the data platforms ZoomInfo runs: streaming and messaging (Kafka/Confluent, Pub/Sub), operational databases (PostgreSQL/Cloud SQL, MongoDB), analytical stores (BigQuery, Snowflake), search (Elasticsearch/Solr), pipeline orchestration and processing (Apache Airflow/Cloud Composer, Dataflow/Apache Beam), and open table formats (Apache Iceberg). Ship them as Terraform modules, GitOps workflows, and documented standards that data teams can adopt without reinventing them. Build the data paved road. Define CI/CD and GitOps patterns for data jobs and pipelines, and partner with platform engineering and data engineering to make data delivery as safe and automated as application delivery. Establish performance, cost, and observability patterns. Give data teams the patterns and guidance to run stores and pipelines efficiently (query and storage optimization, partitioning and clustering, tiering and retention, workload isolation, right-sizing) and the monitoring, alerting, and SLO practices that make performance and cost visible. Set the baseline patterns for backup, restore, and disaster recovery of data stores, including RTO/RPO targets. Partner with FinOps on data-layer spend. Review workloads with teams against these patterns and help them align. Run structured POCs of emerging data technologies and vendor offerings as part of the procurement and technology-lifecycle process.



Recent examples include graph and distributed SQL databases, stream processing (Apache Flink, Confluent Flink), and schema management and cataloging. Bring managed data services inside the perimeter. Define the patterns for connecting vendor-managed data planes (Confluent Cloud, Snowflake, MongoDB Atlas, and similar) to our GCP environment: private connectivity (Private Service Connect, PrivateLink, VPC peering), VPC Service Controls, identity and encryption across the boundary (workload identity, CMEK, in-transit), and the DNS and routing that go with them. Support data consolidation and governance efforts. Contribute infrastructure guidance to data engineering's work on consolidating where data lives and is accessed (BigQuery, Snowflake, object storage) and on data classification, residency, and access policy. Build the reference implementation, write the ADR, and partner with the owning teams to roll the pattern out. Bring a consistent, documented rubric to weekly architecture reviews across all domains, including designs that have nothing to do with data. Expand the standards library and ADR catalog. Serve as the infrastructure consultant to data teams making complex choices. Cloud infrastructure foundation. Production experience architecting on GCP and/or AWS beyond the data services: compute and Kubernetes (GKE or equivalent) as the runtime for data workloads, VPC networking and private connectivity to managed services, IAM and workload identity, and cost and reliability trade-offs. You can review a service-mesh or network design in architecture review with credibility. Hands‑on data infrastructure experience at scale. You've built and operated the infrastructure under data platforms at

📌 Principal Infrastructure Architect: Data Platform (Toronto)
🏢 ZoomInfo Technologies
📍 Toronto