Senior Software Engineer – Data Engineering

About this role:

Wells Fargo is seeking a Senior Software Engineer – Data Engineering

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
• 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

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:

• 4+ 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, vecto…