Senior Data Engineer

Wells Fargo is seeking a Senior Data Engineer to design, build, and operate reusable data capabilities on the Google Cloud Platform, supporting machine learning and AI workloads at enterprise scale. The role emphasizes scalable and secure data pipelines, governance by design, and infrastructure that enables training, validation, and monitoring use cases across the organization.

This hybrid position is based in San Francisco, CA, with a team operating on a schedule of 3 days in office and 2 days remote.

Responsibilities

• Develop scalable, secure data pipelines moving data from on-premise systems of record to GCP services including BigQuery, BigTable, Dataflow, Dataproc, Pub/Sub, Cloud Storage, and Composer.
• Leverage and extend organization-wide capability roadmaps for reusable frameworks and tooling across ingestion, transformation, quality, and orchestration.
• Enable self-service data consumption and governance by standardization through patterns, templates, and sandbox capabilities rather than one-off pipelines.
• Support training, validation, and monitoring use cases using tools such as BigQuery, Dataflow/Apache Beam, Dataproc/Spark, Pub/Sub, and Cloud Storage.
• Create standardized feature transformation pipelines and a common feature store with strong lineage, a dictionary, and high availability for models.
• Ensure cost, performance, and reliability for GCP data workloads using strategies such as partitioning, clustering, storage classes, and autoscaling.
• Develop transformation libraries using Python/SQL/Beam including examples such as SCD patterns, data quality checks, and masking/tokenization routines.
• Provide orchestration through Cloud Composer or Cloud Workflows, using reusable DAGs or templates and CI/CD integration.
• Implement robust data modeling approaches (including dimensional, data vault, or canonical models) and semantic layer implementations using BigQuery or similar tools.
• Enforce data quality, lineage, and observability with standardized metrics, validation rules, and monitoring dashboards.
• Partner with data scientists and domain solution teams to migrate existing models onto GCP capabilities.
• Document patterns, runbooks, and best practices, and provide enablement via workshops and code examples.
• Be flexible to provide both application development and production support during off-hours.

Requirements

• 4+ years of Data Engineering experience, or equivalent demonstrated through a combination of work experience, training, military experience, and/or education.
• 4+ years building analytics or data science solutions in Public Cloud (GCP, AWS, or Azure).
• 4+ years of hands-on experience with Python and/or Go for building data pipelines, libraries, and automation tooling.
• 4+ years with GCP or equivalent open source orchestration tools (Composer/Airflow/Dataflow/Beam) and CI/CD (Git, Liquibase) for data workloads.
• 2+ years hands-on experience building and implementing predictive AI models using machine learning algorithms (e.g., regression, classification, forecasting).

Technologies

• Google Cloud Platform (GCP), BigQuery, BigTable, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer
• Apache Beam, Spark, Cloud Workflows
• Python, SQL, Go, Cloud Composer, Cloud Logging, Cloud Monitoring
• Vertex, Bedrock, SageMaker, Jupyter, Hugging Face, TensorFlow, XGBoost, Anaconda, MLFlow, PyTorch, Scikit-learn
• Git, Liquibase

Benefits

• Health benefits
• 401(k) Plan
• Paid time off
• Disability benefits
• Life insurance, critical illness insurance, and accident insurance
• Parental leave
• Critical caregiving leave
• Discounts and savings
• Commuter benefits
• Tuition reimbursement
• Scholarships for dependent children
• Adoption reimbursement

Desired Qualifications

• Experience with logging and monitoring stacks including Cloud Logging, Cloud Monitoring, error reporting, and metrics dashboards.
• Experience with automated testing, data quality checks, pipeline monitoring, and model governance such as drift, bias, and anomaly detection.
• Experience with model development and operations technologies including Vertex, Bedrock, SageMaker, Jupyter, Hugging Face, TensorFlow, XGBoost, Anaconda, MLFlow, PyTorch, and Scikit-learn.
• Experience with modeling techniques such as clustering, classification, logistic regression, natural language processing, neural networks, ensembling, computer vision, and time-series analysis.
• Experience with data optimization and availability in generative AI solutions such as RAGs, knowledge graphs, MCPs, vectors, prompt validation and tuning environments.

Job Details

• Location: San Francisco, CA (hybrid)
• Salary: USD 100,000 - 196,000 per year
• Hybrid schedule: 3 days in office, 2 days remote
• Visa Sponsorship: Not eligible for Visa Sponsorship
• Posting end date: 8 Oct 2026 (may come down early due to volume of applicants)

Additional Information

• Applicants with disabilities can request a medical accommodation during the application or interview process via Disability Inclusion at Wells Fargo.
• Wells Fargo maintains a drug-free workplace; see the Drug and Alcohol Policy for details.
• Third-party recordings are prohibited unless authorized by Wells Fargo.
• Wells Fargo requires candidates to directly represent their own experiences during recruiting and hiring.
• Job posting locations include 333 Market St, San Francisco, CA 94105 and 1525 W W T Harris Blvd., Charlotte, NC 28262.