Job Description
About this role: Wells Fargo is seeking a Senior Data Engineer. In this role, you will: Lead moderately complex initiatives within Technology and contribute to large scale data processing framework initiatives related to enterprise strategy deliverables Build and maintain optimized and highly available data pipelines that facilitate deeper analysis and reporting Review and analyze moderately complex business, operational or technical challenges that require an in-depth evaluation of variable factors Oversee the data integration work, including developing a data model, maintaining a data warehouse and analytics environment, and writing scripts for data integration and analysis Resolve moderately complex issues and lead teams to meet data engineering deliverables while leveraging solid understanding of data information policies, procedures and compliance requirements Collaborate and consult with colleagues and managers to resolve data engineering issues and achieve strategic goals Required Qualifications: 4+ years of Data Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Job Expectations: We are seeking a highly skilled Senior Data Engineer with 6-10 years of hands-on experience in building, optimizing, and maintaining large-scale data processing systems. The ideal candidate will have strong expertise in Spark-based data engineering, ETL development (preferably Informatica, IDMC), Oracle databases, and streaming technologies like Flink and Kafka, along with solid cloud data handling using Amazon S3 and working knowledge of MongoDB. This role requires strong problem-solving skills, a deep understanding of data pipelines (batch and streaming), and the ability to collaborate effectively with cross-functional teams. Key Responsibilities Design, develop, and maintain scalable and reliable data pipelines using Apache Spark for batch and near-real-time processing. Build, enhance, and support ETL workflows, preferably using Informatica, ensuring data quality, performance, and reliability. Develop and maintain integrations with Oracle databases, including data modeling, performance tuning, and query optimization. Implement and manage streaming data pipelines using Apache Kafka and Apache Flink for real-time data ingestion and processing. Work with Amazon S3 and cloud-based storage solutions for large-scale data ingestion, transformation, and archival. Utilize MongoDB for flexible, document-based data storage and retrieval where applicable. Collaborate with data architects, analysts, and business stakeholders to translate data requirements into technical solutions. Ensure best practices around data governance, security, monitoring, and operational excellence. Troubleshoot production issues, perform root cause analysis, and drive performance improvements. Mentor junior data engineers and contribute to code reviews and design discussions. Mandatory Technical Skills Apache Spark (Core, SQL, DataFrames/Datasets) ETL tools – Preferably Informatica Databases: Oracle (mandatory) Streaming Technologies: Apache Kafka and Apache Flink Cloud Storage: Amazon S3 NoSQL: Working knowledge of MongoDB Additional / Preferred Skills Strong proficiency in SQL and data modeling concepts Experience with Linux/Unix environments Knowledge of data validation, reconciliation, and logging frameworks Exposure to CI/CD pipelines and version control tools like Git Familiarity with cloud platforms (AWS preferred) beyond S3 is a plus Understanding of data warehousing and lakehouse architectures Required Qualifications Bachelor’s degree in Computer Science, Engineering, or a related field 6-10 years of professional experience in data engineering or related roles Proven experience handling large-scale, high-volume data systems Soft Skills Strong analytical and problem-solving abilities Excellent communication and stakeholder management skills Ability to work independently in a fast-paced environment Team-oriented mindset with mentoring capabilities
