Senior Data Platform Infrastructure Engineer – Off-420Role Summary

Build and operate the foundational infrastructure and shared platform services that power our Target State x Data Platform. Own the infrastructure “facades” that make the platform secure, reliable, and easy to use—networking, security, identity and access management, observability/monitoring, and CI/CD—while enabling performant, cost-efficient operation across Databricks, Snowflake, Iceberg, and other query/compute engines.

Key Responsibilities

• Provision, operate, and lifecycle-manage platform compute and storage for Databricks, Snowflake, Spark, Trino, federated query engines, and unstructured data computing engines (capacity planning, upgrades, patching, and environment standardization)
• Design and maintain platform networking foundations (VPC/VNet patterns, routing, private connectivity, DNS, firewalling) to enable secure access between producers, consumers, and managed services
• Implement and operate security and identity management capabilities (authentication/authorization integration, role-based access controls, secrets management patterns, and policy guardrails) in partnership with enterprise security teams
• Build and maintain CI/CD pipelines, infrastructure-as-code, and automation “golden paths” for repeatable provisioning, deployment, and release management
• Optimize cluster/workload performance, auto-scaling, and cost efficiency; establish Fin Ops practices (chargeback/showback, tagging standards, and cost controls)
• Operate and harden catalog and storage layers (Unity Catalog, Polaris, Iceberg-based storage), including access patterns, reliability, and cross-engine interoperability
• Build and operate observability services (monitoring, alerting, audit logging, dashboards) and define SLO/SLA-aligned operational coverage for platform components
• Own platform health reporting and operational readiness: define, track, and publish SLA/SLO metrics (availability, latency, data/compute freshness where applicable), establish health checks, and drive continuous improvement based on incident trends
• Collaborate with Architecture Governance to ensure platform designs and implementations meet security and compliance requirements (control alignment, evidence generation, and periodic reviews)
• Enable multi-engine interoperability and federation by standardizing configurations, connectivity, and operational practices across engines
• Partner with Data Engineering teams on onboarding, deployment readiness, reliability improvements, and production troubleshooting for data pipelines and workloads
• Define platform guardrails and operational runbooks (backup/restore, DR patterns, change management), and participate in on-call/incident response for infrastructure services

Required Qualifications

• Strong experience in cloud engineering(AWS, GCP and/or Azure) including compute, storage,networking (VPC/VNet, routing, private connectivity), and secure service integrations
• Proficiency with infrastructure-as-code and automated provisioning using Terraform and/or CloudFormation, including environment standardization and repeatable “golden path” modules
• Hands-on experience with containers and Kubernetes, plus strong CI/CD skills for infrastructure and platform services (build/release automation, change control, and pipeline security)
• Experience with monitoring/observability platforms (Splunk/Datadog,CloudWatch,Prometheus/Grafana) including SLO/SLA instrumentation, alerting, dashboards, and audit logging
• Proven production operations experience for data platforms (on-call/incident response, root-cause analysis, runbooks, platform health reporting, and DR/backup-restore patterns)
• Strong foundation in platform security and compliance: IAM/RBAC concepts, secure networking patterns, secrets management practices, and experience supporting compliance control alignment and evidence generation with Architecture Governance/Security partners
• Working knowledge of operating lakehouse/data platform components across Databricks,Snowflake,Trino, and Iceberg-based storage, including interoperability, connectivity, and access patterns
• Experience optimizing performance and cost of compute and analytical workloads (auto-scaling, workload tuning, tagging/chargeback/showback, and cost controls)

About State Street

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