Principal Engineer - Data Engineering

Wells Fargo & Company•Irving, TX

1d•Hybrid

About The Position

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. The role leads the development of secure, scalable, cloud-native data solutions that enable operational intelligence, regulatory reporting, advanced analytics, and data-driven decision making across the enterprise. The Principal Engineer provides technical leadership across multiple engineering teams, establishes enterprise data engineering standards, and advances modern data architectures, governance practices, and metadata-driven solutions. The role partners with business and technology stakeholders to deliver resilient, high-performing data capabilities aligned with Wells Fargo's strategic objectives. This position offers a hybrid work schedule and requires the employee to work on-site at one of the listed locations. The position is not eligible for visa sponsorship.

Requirements

7+ years of engineering experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination of these.
5+ years 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 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.

Nice To Haves

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.
Strong 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.
Experience supporting audit, regulatory, and data governance programs.
Strong experience leading globally distributed engineering teams across onshore and offshore delivery models.
Experience evaluating and adopting emerging technologies, including AI/ML-enabled data engineering and intelligent automation solutions.
Advanced understanding of enterprise architecture frameworks and technology governance practices.

Responsibilities

Define and drive enterprise data engineering strategy, architecture standards, and technology roadmaps.
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 standards, design patterns, and best practices for batch, real-time, and event-driven data processing.
Drive cloud migration, platform consolidation, and legacy data platform modernization initiatives.
Design scalable data solutions using distributed processing frameworks such as Spark and cloud-native analytics services.
Lead technical architecture reviews and provide engineering governance for 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 scalability, performance, high availability, and disaster recovery requirements.
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 that support 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 on root cause analysis and platform stability improvements.