Principal Platform Engineer — Data Private Cloud (Kubernetes/OpenShift)

Principal Platform Engineer — Data Private Cloud (Kubernetes/OpenShift)

Wells Fargo is back in the office three days a week, collaborating for fabulous outcomes!

This role has no visa sponsorship or visa transfers.

We’re seeking a Principal Platform Engineer to lead the technical strategy, architecture, and delivery of Wells Fargo’s enterprise Data Private Cloud. This is a Kubernetes/OpenShift platform engineering role, responsible for designing and operating the large‑scale infrastructure that powers data, analytics, and AI workloads across the company.

This is not a data engineering role. You won’t be building pipelines or analytics solutions.

Instead, you will:

• Architect the Kubernetes-based data platform
• Build infrastructure, automation, and security foundations
• Define standards, controls, and multi‑tenant patterns
• Enable data engineers, ML engineers, and analytics teams by providing a scalable, secure platform they run on

This is a hands-on senior engineering role with end-to-end ownership of platform architecture.

Core Responsibilities

Technical Leadership & Architecture

• Own the architecture of the enterprise data platform (OpenShift, Kubernetes, modern data stacks)
• Define platform standards for security, scalability, multi-tenancy, and operational excellence
• Lead decisions around compute orchestration (Spark on K8s, YuniKorn), query federation (Trino, Kyuubi), and metadata management (Gravitino, Hive Metastore)
• Design authentication/authorization (Keycloak, AD, Ranger)
• Shape infrastructure strategy and open-source deployment patterns

Infrastructure & Platform Engineering

• Lead Terraform-based IaC and repeatable deployment practices
• Architect networking, ingress, and service mesh configurations
• Oversee PKI, SSL/TLS, and certificate lifecycle management
• Build monitoring and observability strategies (OpenSearch, Prometheus, Grafana)
• Ensure resilience through scheduling, quotas, and capacity planning
• Implement GitOps for declarative deployments

Data Platform Components (Platform Enablement, Not Data Engineering)

You provide leadership for the platform that runs these technologies, not the pipelines or applications built on them:

• Compute: Spark on K8s, Kyuubi, JupyterHub
• Query/Analytics: Trino, Superset
• Orchestration: Airflow on Kubernetes
• Catalog/Governance: Gravitino, DataHub, Ranger
• Storage: Iceberg, S3/NetApp, PostgreSQL
• Messaging/Search: Kafka, OpenSearch

Security & Compliance

• Ensure compliance with regulatory requirements (OSFI, SOX, PCI-DSS)
• Implement multi-tenant isolation and robust security boundaries
• Lead security reviews, threat modeling, and remediation
• Partner with Security, Risk, and Compliance teams on audits and controls

Required Qualifications

• 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 5+ years of hands-on experience with Kubernetes in production environments (OpenShift Container Platform strongly preferred)
• Proven track record designing and operating large-scale data platforms in enterprise environments

Preferred Qualifications

• Experience in financial services or other highly regulated industries
• Experience with Kubernetes scheduling frameworks (YuniKorn, Volcano) for batch and AI workload optimization
• Contributions to open-source projects in the data or infrastructure
• Experience building and deploying applications with enterprise data sources
• Hands-on experience with transformer architectures and fine-tuning open-source models
• Professional certifications: CKA/CKAD, AWS/Azure/GCP Professional, Terraform Associate
• Experience with GitOps practices (ArgoCD, Flux)
• Background in platform product management or developer experience

Technical Expertise

Infrastructure & Orchestration

• Expert-level Kubernetes knowledge: deployments, stateful workloads, operators, CRDs, RBAC, network policies, storage classes
• OpenShift Container Platform: Routes, SCCs, cluster administration, operator lifecycle management
• Infrastructure-as-code with Terraform (modules, state management, provider development)
• Container runtimes, image registries, and CI/CD pipeline integration

Data Platform Technologies

• Apache Spark: architecture, tuning, Spark on Kubernetes, dynamic resource all