About this role:

Wells Fargo is seeking a Lead Software Engineer – Data Engineering to join the GFCM – Global Funding and Collateral Management Data Engineering team within the Enterprise Functions Technology (EFT) organization. In this role, you will be responsible for designing, developing, optimizing, and maintaining metadata‑driven, scalable, high‑performance data engineering frameworks that power critical financial risk processes across Corporate Treasury.

In this role, you will:

• Lead complex technology initiatives including those that are companywide with broad impact

• Act as a key participant in developing standards and companywide best practices for engineering complex and large scale technology solutions for technology engineering disciplines

• Design, code, test, debug, and document for projects and programs

• Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives, enterprise technological environment, and technical challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors

• Make decisions in developing standard and companywide best practices for engineering and technology solutions requiring understanding of industry best practices and new technologies, influencing and leading technology team to meet deliverables and drive new initiatives

• Collaborate and consult with key technical experts, senior technology team, and external industry groups to resolve complex technical issues and achieve goals

• Lead projects, teams, or serve as a peer mentor

Required Qualifications:

• 5+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

• Design, build, test, deploy, and maintain large-scale structured and unstructured data pipelines using Python, SQL, Apache Spark, and modern data lake/lakehouse technologies.
• Develop and optimize metadata-driven pipelines, wrappers, ingestion frameworks, and validation layers to support GFCM data workflows.
• Build and maintain high-quality ELT/ETL pipelines following best practices in reliability, performance, observability, and reusability.
• Distributed Computing & Lakehouse Engineering
• Engineer and optimize Spark pipelines for large‑scale batch and streaming workloads (partitioning, caching, Catalyst optimization, AQE, Tungsten).
• Work with open table formats such as Iceberg, Delta, or Hudi for versioned data, time-travel, compaction, and schema evolution.
• Implement Medallion (Bronze/Silver/Gold) architecture patterns for modern lakehouse systems.
• Data Quality, Testing & Observability
• Implement automated data quality frameworks using tools such as Great Expectations / Deequ or custom validators.
• Build data health monitoring frameworks with SLAs/SLOs, anomaly detection, and lineage capture.
• Ensure strong validation layers during Data Center exits, migration programs, and DPC onboarding.
• API & Microservice Development
• Build RESTful and metadata APIs using Python frameworks (FastAPI/Flask) to enable secure, governed data access.
• Collaborate with application teams to integrate data access patterns and platform services.
• Cloud & Platform Engineering
• Design and deploy data pipelines in cloud platforms (AWS, Azure, GCP) leveraging managed compute, orchestration, and storage.
• Build CI/CD workflows and infrastructure automation using Jenkins, GitHub Actions, Azure DevOps, Terraform, or Helm.
• Apply secure engineering principles including IAM, secrets management, encryption standards, and network controls.
• Orchestration & Scheduling
• Build resilient orchestration flows using Autosys or equivalent tools.
• Apply modular design with retries, alerts, SLAs, and workflow dependency management.
• Collaboration & Delivery
• Work with cross-functional Agile teams (Product, Architecture, QA, Treasury SMEs).
• Analyze technical requirements, evaluate design alternatives, and provide recommendations aligned with enterprise standards.
• Independently deliver complex engineering tasks and contribute to architecture/roadmap discussions.

Job Expectations:

• 5+ years of hands-on experience with Python, SQL, and bash scripting for automation.
• Strong experience building big data pipelines using Apache Spark, Hive, Hadoop.
• Experience with Autosys/Airflow or similar orchestration tools.
• Working knowledge of REST APIs, Object Storage, Dremio, and CI/CD pipelines.
• Strong troubleshooting and problem‑solving capabilities.
• Solid foundation in data modeling (conceptual/logical/physical) and database design.
• Experience architecting pipelines using distributed systems patterns (shuffle optimization, spill, broadcast, storage layouts).
• Experience with streaming frameworks like Spark Structured Streaming or Apache Flink.
• Data Lakehouse & Storage
• Hands‑on with optimization techniques: clustering, Z‑ordering, vectorized IO (Parquet/ORC), compaction.
• Experience implementing Medallion architectures and governed ingestion zones.
• Data Quality & Governance
• Knowledge of data governance platforms (Collibra, Alation, Purview).
• Understanding of financial data controls, validation rules, reconciliation checks, and compliance (SOX/PCI).
• Experience implementing lineage, observability, drift detection.

Platform & DevOps

• Cloud-native engineering experience — serverless, managed Spark, event-driven architectures.
• Familiarity with containerization (Docker, K8s) and workflow operators.
• Strong experience implementing test automation for data pipelines (unit, contract, integration tests).
• GenAI for Data Engineering
• Applying GenAI for metadata extraction, data anomaly detection, automated documentation, or pipeline optimization.
• Domain Expertise
• Exposure to financial risk, treasury functions, or Collateral Management processes.

Posting End Date:

*Job posting may come down early due to volume of applicants.

We Value Equal Opportunity

Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.

Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.

Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.

Applicants with Disabilities

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

Drug and Alcohol Policy

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

Wells Fargo Recruitment and Hiring Requirements:

a. Third-Party recordings are prohibited unless authorized by Wells Fargo.

b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.