Wells Fargo
Financial Services
LeadDataEngineer
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“Lead Data Engineer at Wells Fargo. Skills: Data Engineering, Google Cloud Platform, Data Architecture, Cloud-native data ecosystem. Design scalable data platforms. Implement scalable data platforms”
Industry & Context.
Root cause analysis; Troubleshooting; Optimization
Production support off-hours, Hybrid schedule
What They're Looking For.
Must Have
5+ years Database Engineering experience, 5+ years data management in Public Cloud, 5+ years Python or Java, 5+ years Spark SQL, 5+ years orchestration tools, 5+ years CI/CD
Nice to Have
Experience with logging/monitoring stacks, Experience with automated testing, Experience with data quality checks, Experience with monitoring for pipelines, Knowledge of cloud architecture principles, Experience with core GCP data services, Experience with Agile transformations, Experience with technology roadmaps, Experience working with onshore and offshore teams
What You'll Do.
Design scalable data platforms
Implement scalable data platforms
Design secure data platforms
Implement secure data platforms
Build reusable frameworks
Build reusable tooling
Enable self-service data consumption
Enable self-service data governance
Design logical data platform architectures
Design physical data platform architectures
Define ingestion patterns
Implement ingestion patterns
Define transformation patterns
Implement transformation patterns
Define serving patterns
Implement serving patterns
Optimize GCP data workload cost
Optimize GCP data workload performance
Optimize GCP data workload reliability
Build opinionated data ingestion frameworks
Develop shared transformation libraries
Provide orchestration capabilities
Implement data modeling
Implement semantic layers
Enforce data observability
Apply security controls
Apply governance controls
Partner with data engineers
Partner with analytics teams
Partner with ML teams
Document best practices
Contribute to platform roadmap
Contribute to tool selection
Evaluate new GCP services
Evaluate open-source components
How You'll Work.
Team & Collaboration
Multiple product teams; Domain teams; Domain data engineers; Analytics teams; ML teams
Communication Scope
Workshops; Code examples
Process & Methodology
Agile transformations, Technology roadmaps
Full Job Description
**About this role:** Within COO Technology, Wells Fargo is seeking a Lead Data Engineer to help shape and scale our cloud‑native data ecosystem. In this role, you will focus on Google Cloud Platform (GCP) services and frameworks, leading the design, build, and operation of reusable data capabilities that power analytics and AI at enterprise scale. The ideal candidate is passionate about standardized frameworks, self‑service data platforms, and governance‑by‑design, enabling secure, reliable, and compliant data solutions across Google Cloud services. The COO Technology group powers the firm’s most critical operations by modernizing and optimizing enterprise technology platforms that enable resiliency, regulatory excellence, data services, customer experience, and strategic execution across the Chief Operating Office. **In this role, you will:** * Design and implement scalable, secure data platforms on Google Cloud using managed services (BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer).) * Build reusable frameworks and tooling (ingestion, transformation, quality, orchestration) that can be adopted by multiple product and domain teams. * Enable self‑service data consumption and governance by standardizing patterns, templates, and platform capabilities rather than one‑off pipelines. * Design logical and physical data platform architectures leveraging BigQuery, Dataflow/Apache Beam, Dataproc/Spark, Pub/Sub, and Cloud Storage. * Define and implement standardized ingestion, transformation, and serving patterns (batch and streaming) as reusable blueprints. * Optimize cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies). * Build opinionated data ingestion frameworks (e.g., config‑driven pipelines, connectors, schema handling, error handling) on top of Dataflow, Dataproc, or Composer. * Develop shared transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality chec
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