Lead Data Engineer - PySpark/SQL/AI

Wells Fargo is seeking a Lead Data Engineer.

In this role, you will:

• Lead complex initiatives with broad impact and act as key participant in large scale software planning for the Technology area
• Design, develop, and run tooling to discover problems in data and applications and report the issues to engineering and product leadership
• Review and analyze complex software enhancement initiatives for business, operational or technical improvements that require in depth evaluation of multiple factors including intangibles or unprecedented factors
• Make decisions in complex and multi-faceted data engineering situations requiring understanding of software package options and programming language and compliance requirements that influence and lead Technology to meet deliverables and drive organizational change
• Strategically collaborate and consult with internal partners to resolve highly risky data engineering challenges

• 5+ years of Database Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
• 5 - 10+ years of experience in Data Engineering and Enterprise Data Platforms.
• 5+ years leading enterprise-scale data modernization initiatives.
• Strong hands-on expertise with:
  • Python
  • Apache Spark / PySpark
  • SQL
  • ETL/ELT Design
  • API Integration
• Deep understanding of:
  • Lakehouse Architecture
  • Medallion Architecture
  • Data Warehousing Concepts
  • Data Governance
  • Metadata Management
  • Data Quality Frameworks
• Proven experience with any cloud platforms:
  • Azure
  • Microsoft Fabric
  • Databricks
  • GCP
  • Experience migrating large-scale data environments from on-premise to cloud.
• Experience in HR, Workforce, Talent, Payroll, or Enterprise Data domains.
• Exposure to AI/ML, Generative AI, Agentic AI, and LLM-based application development.
• Experience implementing Data Products, Data Mesh, and Self-Service Analytics platforms.
• Knowledge of Kubernetes, Docker, Terraform, and Infrastructure as Code.
• Familiarity with MLOps, DataOps, and DevSecOps practices.
• Cloud certifications in Azure, Databricks, GCP, or equivalent.

We are seeking a highly experienced Modern Data Engineering Lead to drive the modernization of HR Data platforms and accelerate the adoption of Cloud, AI, and Data Engineering best practices. This role will lead the transformation of large-scale legacy data ecosystems into scalable, cloud-native, AI-enabled platforms while enabling advanced analytics, data products, automation, and self-service capabilities.

The ideal candidate will possess deep expertise in Cloud Data Platforms, Lakehouse Architecture, Data Migration, AI-powered Engineering, Python, Spark, API integration, and Enterprise Data Modernization. This leader will work closely with business stakeholders, architects, product teams, and engineering organizations to deliver strategic outcomes across HR Data systems.

• Lead enterprise-scale modernization initiatives from legacy data warehouses and ETL platforms to cloud-native data ecosystems.
• Design and implement modern Lakehouse architectures using Medallion (Bronze, Silver, Gold) data design principles.
• Develop scalable and secure data platforms supporting batch, streaming, and real-time processing workloads.
• Drive architecture decisions involving Data Lake, Data Warehouse, Lakehouse, Data Mesh, and Data Fabric patterns.

• Design, develop, and optimize large-scale data pipelines using:
  • ETL/ELT frameworks
• Implement cloud-native solutions leveraging platforms such as:
• Establish engineering standards for reliability, scalability, observability, and cost optimization.

• Lead large-scale migration programs involving:
  • Legacy Data Warehouses
  • On-Prem Hadoop/Cloudera Platforms
  • Traditional ETL Middleware
• Develop migration strategies including assessment, planning, automation, reconciliation, testing, and cutover execution.
• Ensure business continuity and data integrity during migration programs.