Principal Engineer - Data Engineering

About the role

Wells Fargo's Shared Service Operations Technology (SSOT) organization is seeking a Principal Engineer - Data Engineering to drive the architecture, engineering strategy, and modernization of enterprise-scale data platforms and data products. This leader will play a critical role in building secure, scalable, cloud-native data solutions that enable operational intelligence, regulatory reporting, advanced analytics, and data-driven decision making across the enterprise. The ideal candidate brings deep expertise in large-scale data engineering, distributed processing, data lakehouse architectures, and real-time and batch data platforms. In this role, you will provide technical leadership across multiple engineering teams, establish enterprise data engineering standards, and drive the adoption of modern data architectures, governance practices, and metadata-driven solutions. You will partner closely with business and technology stakeholders to deliver resilient, high-performing data capabilities that support Wells Fargo's strategic objectives.

Responsibilities

• Define and drive the enterprise data engineering strategy, architecture standards, and technology roadmap.
• Lead the design and implementation of scalable cloud-native data platforms, data lakes, data warehouses, and lakehouse architectures.
• Architect enterprise-grade data ingestion, transformation, conformance, and data product delivery pipelines.
• Establish engineering standards, design patterns, and best practices for batch, real-time, and event-driven data processing.
• Drive modernization initiatives involving cloud migration, platform consolidation, and legacy data platform transformation.
• Design highly scalable data solutions leveraging distributed processing frameworks such as Spark and cloud-native analytics services.
• Lead technical architecture reviews and provide engineering governance across critical data initiatives.
• Establish enterprise data modeling standards for operational, analytical, dimensional, and domain-oriented architectures.
• Define and implement metadata-driven and configuration-driven engineering frameworks.
• Design resilient data platforms that meet enterprise requirements for scalability, performance, high availability, and disaster recovery.
• Partner with Enterprise Architects, Product Owners, Platform Engineering teams, and Business Leaders to align technology investments with business objectives.
• Drive implementation of data governance, lineage, metadata management, and data quality frameworks.
• Lead adoption of DataOps, DevOps, CI/CD, Infrastructure-as-Code, and engineering automation practices.
• Define observability, monitoring, alerting, and operational excellence standards for enterprise data platforms.
• Design solutions supporting regulatory reporting, auditability, security, privacy, and compliance requirements.
• Evaluate emerging technologies