Senior Software Engineer

About this role:

Wells Fargo is seeking a Senior Software Engineer to join a team within the Chief Data Office (CDO) responsible for building and supporting a highly scalable, secure, and cloud-native data platform that powers enterprise analytics, big data processing, and emerging AI/ML capabilities. In this role, you will help design, engineer, and maintain critical platform infrastructure supporting technologies such as OpenShift, Kubernetes, Apache Spark, Trino, Iceberg, and object storage solutions.

The ideal candidate will combine expertise in software engineering, platform engineering, and large-scale data ecosystems to develop automated solutions, optimize system performance, and ensure platform reliability. Responsibilities include managing containerized environments, building automation and observability frameworks, implementing security controls, supporting batch and streaming data pipelines, and driving the operational excellence of a modern enterprise data platform. This role offers the opportunity to work at the intersection of cloud infrastructure, big data, and next-generation AI/ML technologies while enabling data-driven innovation across the organization.

In this role, you will:

• Lead moderately complex initiatives and deliverables within technical domain environments
• Contribute to large scale planning of strategies
• Design, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments
• Review moderately complex technical challenges that require an in-depth evaluation of technologies and procedures
• Resolve moderately complex issues and lead a team to meet existing client needs or potential new clients needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements
• Monitor for configuration drift and enforce infrastructure policies.
• Build automated regression and performance test suite to ensure health checks of all components of the platform
• Monitor system health and enforce runtime policies.
• Implement and manage security protocols, including Oauth authentication, TLS encryption, and role-based access control (RBAC).
• Conduct regular maintenance, including cluster scaling, perform regular security audits
• Collaborate and consult with peers, colleagues, and mid-level managers to resolve technical challenges and achieve goals
• Lead projects and act as an escalation point, provide guidance and direction to less experienced staff

Required Qualifications:

• 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 4+ years of experience supporting highly available and scalable infrastructure that includes object storage, OpenShift, Spark, Iceberg, YuniKorn, Trino, and related technologies.
• 4+ years of experience configuring, administering, and monitoring Big Data ecosystem components using a variety of BI, monitoring, and observability tools.
• 4+ years of hands-on programming experience in one or more of the following: Python, Bash, Shell, SQL, Java, or Scala.
• 4+ years of experience supporting operating systems and containerized environments, including system programming, performance tuning, networking, orchestration, and deployment using Kubernetes (K8s), Helm, and/or Terraform.
• 4+ years of experience with Big Data technologies and platforms, including Apache Spark, Hadoop, Hive, Trino, Iceberg, Kafka, Flink, Amazon S3, NetApp StorageGRID, and data formats such as Parquet, Avro, ORC, JSON, and CSV.

Desired Qualifications:

• Experience with AI/ML frameworks, Large Language Models (LLMs), and Model Training & Consumption (MTC) platforms, supporting the development, deployment, and management of advanced machine learning solutions.
• Experience building, orchestrating, and optimizing data workflows using ETL and workflow automation tools such as Apache Airflow and Apache NiFi.
• Experience with MLOps platforms and model lifecycle management tools, including MLflow, Kubeflow, and Amazon SageMaker.
• Knowledge of data science and machine learning concepts, including feature engineering, model deployment, model serving, and inference pipeline development.
• Familiarity with NexusOne platform capabilities, architecture, and integrations.
• Understanding of enterprise access control