Senior Data Engineer

Wells Fargo & Company•Charlotte, NC

1d•$100,000 - $196,000•Hybrid

About The Position

The Data, Analytics and Reporting Technology team is responsible for a cross-cutting set of capabilities within the Global Operations at Wells Fargo. The AI/ML Data Architecture, Engineering and Enablement team is seeking a Senior Data Engineer to help create effective solutions for our data scientists ranging from experimentation to monitoring. In this role, you will focus on Google Cloud Platform (GCP) services and frameworks, contributing to the design, build, and operation of reusable data capabilities that power machine learning and AI at enterprise scale. The ideal candidate is passionate about standardized frameworks, self‑service by subject matter experts, and governance‑by‑design, enabling secure, reliable, and compliant data solutions that facilitate the generation of new intelligence and operating efficiencies.

Requirements

4+ years of Data Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
4+ years of experience creating analytics or data science solutions in Public Cloud (GCP, AWS, Azure)
4+ years of hands on experience of 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 of hands-on experience building and implementing predictive AI models using machine learning algorithms (e.g., regression, classification, forecasting).

Nice To Haves

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 such as Vertex, Bedrock, Sagemaker, Jupyter, Hugging Face, TensorFlow, XGBoost, Anaconda, MLFlow, PyTorch, Scikit-learn
Experience with modelling techniques such as 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

Responsibilities

Develop scalable, secure data pipelines from on-premise systems of record to Google Cloud Platform services (BigQuery, BigTable, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer).
Leverage and extend capability roadmaps for reusable frameworks and tooling (ingestion, transformation, quality, orchestration) actively being developed by the larger organization.
Enable self‑service data consumption and governance by standardizing patterns, templates, and sandbox capabilities rather than one‑off pipelines.
Support use cases for training, validation and monitoring leveraging BigQuery, Dataflow/Apache Beam, Dataproc/Spark, Pub/Sub, and Cloud Storage.
Create standardized feature transformation pipelines and a common feature store with strong lineage, dictionary and high availability for models.
Ensure appropriate cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies).
Develop transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality checks, masking/tokenization routines).
Provide orchestration capabilities via Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration.
Implement robust data modeling (dimensional, data vault, or canonical models) and semantic layer implementations with BigQuery or similar tools.
Enforce data quality, lineage, and observability using 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 through workshops and code examples.