Senior Data Engineer

Wells Fargo is seeking a Senior Data Engineer to design, build, and operate reusable data capabilities on Google Cloud Platform that support machine learning and AI at enterprise scale.

Responsibilities

• Develop scalable, secure data pipelines from on-premise systems of record to GCP services, including BigQuery, BigTable, Dataflow, Dataproc, Pub/Sub, Cloud Storage, and Composer.
• Leverage and extend organization-level capability roadmaps for reusable frameworks and tooling (ingestion, transformation, quality, orchestration).
• Enable self-service data consumption and governance by design using standard patterns, templates, and sandbox capabilities instead of one-off pipelines.
• Support training, validation, and monitoring use cases using 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 partitioning, clustering, storage classes, and autoscaling strategies.
• Develop transformation libraries in Python/SQL/Beam, including examples such as common SCD patterns, data quality checks, and masking/tokenization routines.
• Provide orchestration capabilities via Cloud Composer or Cloud Workflows using reusable DAGs/templates and CI/CD integration.
• Implement robust data modeling approaches (dimensional, data vault, or canonical models) and semantic layer implementations using BigQuery or similar tools.
• Enforce data quality, lineage, and observability through 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; provide enablement through workshops and code examples.
• Be flexible to provide application development and production support during off-hours.

Requirements

• 4+ years of Data Engineering experience, or equivalent demonstrated through one or a combination of: work experience, training, military experience, or education.
• 4+ years experience creating analytics or data science solutions in Public Cloud (GCP, AWS, Azure).
• 4+ years hands-on experience with Python and/or Go for building data pipelines, libraries, and automation tooling.
• 4+ years working 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, Cloud Composer, Cloud Logging, Cloud Monitoring
• Apache Beam, Spark, Cloud Workflows
• Vertex, Bedrock, SageMaker
• Python, SQL, Beam, Go
• 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/monitoring stacks: Cloud Logging, Cloud Monitoring, error reporting, metrics dashboards.
• Experience with automated testing, data quality checks, monitoring for pipelines, 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, Scikit-learn.
• Experience with modeling techniques including clustering, classification, logistic regression, natural language processing, neural networks, ensembling, computer vision, 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 Expectations

• This role is not eligible for Visa Sponsorship.
• Hybrid schedule (3 days in office, 2 days remote).
• Work transparently.
• Take ownership.
• Learn more through regular self-education and skill improvement.
• Embrace change and remain flexible as technology evolves.

Job Posting Locations

• 333 Market St, San Francisco, California 94105
• 1525 W W T Harris Blvd., CHARLOTTE, North Carolina 28262

Salary

USD 100,000 - 196,000 per yearly

Posting End Date

• 8 Oct 2026
• Job posting may come down early due to volume of applicants.

Accessibility

To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.

Policy

• Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.

Recruitment Requirements

• Third-Party recordings are prohibited unless authorized by Wells Fargo.
• Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.