Principal Engineer –Data Platforms (Enterprise Data Platforms, Lakehouse, Multi‑Tenant Architectures)

Core Role Expectation

• Hands-on Principal Engineer responsible for architecting, engineering, and enablement of large-scale, multi-tenant enterprise data platforms.
• Focus on platform architecture, scalability, security, and operational excellence for shared enterprise data platforms.

Required Technical & Leadership Skillset

Enterprise Data Platform Architecture & Engineering

• Extensive experience designing, engineering, and operating enterprise-scale data platforms, including data lakes, lakehouses, or data warehouses.
• Proven experience leading large, multi-tenant data platforms serving multiple lines of business with strict isolation, governance, and performance controls.
• Deep understanding of data platform reference architectures including lakehouse patterns, shared services vs. tenant-owned workloads, and platform-as-a-product operating models.
• Demonstrated ownership of end-to-end platform lifecycle: architecture, build, migration, operations, and modernization.

Multi-Tenancy, Scale & Performance

• Hands-on experience designing and enforcing multi-tenant isolation.
• Expertise in capacity planning, workload isolation, quota management, and performance optimization at enterprise scale.
• Experience supporting mixed workloads (batch, interactive SQL, streaming, ML/AI) on shared platforms.

Data Platform Technologies (Hands-On)

• Strong hands-on expertise with modern data platform ecosystems including:
  • Compute & Processing: Spark (including Spark at scale), distributed processing frameworks.
  • Query & Analytics: Trino/Presto or similar distributed SQL engines.
  • Table Formats & Storage: Iceberg (or similar), Iceberg Rest Catalogue, object storage and enterprise storage platforms.
  • Metadata, Catalog & Governance: DataHub, Apache Atlas, Hive Metastore, or equivalent.
• Experience designing and operating production-grade data services.

Platform Engineering & Automation

• Strong background in platform engineering principles applied to data platforms:
  • Infrastructure as Code (Terraform or equivalent).
  • Automated environment provisioning and repeatability.
  • GitOps or declarative deployment models.
• Experience standardizing and industrializing data platforms to support self-service consumption at scale.

Security, Governance & Compliance

• Demonstrated experience building secure-by-design data platforms in regulated environments.
• Hands-on knowledge of authentication and authorization models (enterprise IAM integration), fine-grained access controls and data entitlements, auditability, lineage, and compliance controls.
• Proven ability to partner with Security, Risk, Compliance, and Audit teams to meet regulatory requirements (e.g., SOX, PCI, data privacy).

Technical Leadership & Influence

• Recognized technical leader capable of setting data platform strategy and standards across the enterprise.
• Making architecture decisions that balance scalability, cost, risk, and time-to-market.
• Mentoring senior engineers and influencing platform adoption across teams.
• Experience leading complex platform migrations or modernizations (e.g., legacy data platforms to modern lakehouse architectures).

Data Platform Components (Platform Enablement)

• 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.

Required Qualifications

• 7+ years of Engineering experience, or equivalent demonstrated through work experience, training, military experience, or 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 / Differentiating Experience

• Experience in financial services or other highly regulated enterprises.
• Prior ownership of enterprise data platform transformations.
• Contributions to open-source data or platform ecosystems.
• Background in platform product thinking or developer experience for data platforms.
• Experience supporting AI/ML workloads on shared enterprise data platforms.

Pay Range

• $159,000.00 - $305,000.00 base pay range.
• Pay may vary depending on prior performance, skills, experience, or work location.
• Employees may be eligible for incentive opportunities.

Benefits

• Health benefits
• 401(k) Plan
• Paid time off
• Disability benefits
• Life insurance, critical illness insurance, and accident insurance
• Parental leave
• Critical caregiving leave
• Discounts and savings
• Commuter benefits
• Tuition reimbursement
• Scholarships for dependent children
• Adoption reimbursement

Equal Opportunity

• Wells Fargo is an equal opportunity employer.
• All qualified applicants will receive consideration without regard to legally protected characteristics.

Additional Information

• Employees support a risk mitigating and compliance-driven culture.
• Emphasis on proactive monitoring, governance, risk identification, and escalation.
• Applicants with disabilities can request medical accommodations.
• Wells Fargo maintains a drug free workplace.
• Third-party recordings are prohibited unless authorized.
• Candidates must represent their own experiences during recruiting and hiring.