Principal Engineer - Data Engineering

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Description

About this 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. In This Role, You Will 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, architectural patterns, and industry best practices to drive innovation. Establish reusable engineering frameworks and accelerators to improve productivity and reduce delivery risk. Provide technical leadership for complex enterprise programs involving multiple delivery teams and business stakeholders. Mentor senior engineers, technical leads, and solution architects while promoting engineering excellence and continuous learning. Collaborate with production support and reliability engineering teams to drive root cause analysis and platform stability improvements. 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 experience designing and implementing Data Lakes, Data Warehouses, Lakehouse Architectures, and Enterprise Data Platforms . 5+ years of expertise in distributed data processing technologies including Apache Spark , Dataproc, Hadoop ecosystems, or similar platforms. 5+ years of experience building cloud-native data solutions using Google Cloud Platform (GCP), AWS, Azure, or equivalent cloud platforms . 5+ years of programming experience using Python, Java, SQL, or similar engineering languages. 5+ years of expertise in ETL/ELT architecture, data integration patterns, and enterprise data movement strategies. 5+ years of knowledge in security, encryption, access controls, and enterprise risk management requirements. Desired Qualifications Experience building cloud-native data platforms on Google Cloud Platform (GCP) . Strong knowledge of BigQuery, BigLake, Apache Iceberg, Lakehouse architectures, Dataproc, Cloud Storage, Cloud Composer, DataPlex Knowledge Catalog, Lakehouse Runtime Catalog and Pub/Sub . Experience leading cross-functional engineering teams and large-scale technology transformation programs. Excellent architecture documentation, communication, and stakeholder management skills. Experience implementing CI/CD and DataOps practices for large-scale data engineering environments. Experience implementing Medallion Architecture , Data Mesh, or Domain-Oriented Data Architecture principles. Experience designing enterprise-scale real-time streaming solutions using Kafka, Pub/Sub, Flink, Spark Streaming, or equivalent technologies. Financial Services, Regulatory Reporting, Financial Crimes, AML, Fraud, KYC, Risk Management, or Compliance domain experience. Experience designing highly available, fault-tolerant, and scalable data platforms. Experience implementing enterprise metadata and data catalog solutions. Demonstrated ability to influence executive stakeholders and drive enterprise-wide technical initiatives. Expertise in data platform observability, monitoring, SRE, and operational excellence practices. Experience with containerized workloads, Kubernetes, and cloud-native orchestration platforms. Experience with Infrastructure as Code using Terraform or similar automation frameworks. Knowledge of modern data sharing, data product, and self-service analytics architectures. Experie

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