Software Engineer ; Data Lake & Data Warehouse Solutions

Software Development Position

At U.S. Bank, we're on a journey to do our best. Helping the customers and businesses we serve to make better and smarter financial decisions and enabling the communities we support to grow and succeed. We believe it takes all of us to bring our shared ambition to life, and each person is unique in their potential. A career with U.S. Bank gives you a wide, ever-growing range of opportunities to discover what makes you thrive at every stage of your career.

Try new things, learn new skills and discover what you excel at—all from Day One.

This position will be responsible for the analysis, design, testing, development and maintenance of best-in-class software experiences. The candidate is a self-motivated individual who can collaborate with a team and across the organization. The candidate takes responsibility of the software artifacts produced adhering to U.S. Bank standards to ensure minimal impact to the customer experience. The candidate will be adept with the agile software development lifecycle and Dev Ops principles.

Essential Responsibilities:

• Design, develop, and maintain enterprise-scale Data Warehouse, Data Lake, and Lakehouse solutions that support operational, analytical, and AI-driven business initiatives.
• Build and optimize scalable data models, database architectures, and storage frameworks to enable reporting, analytics, and operational workloads.
• Develop, enhance, support, and modernize batch, streaming, and near real-time ETL/ELT pipelines using SQL, Python, and cloud-native technologies, including migration of legacy workloads to modern data platforms.
• Integrate and transform structured, semi-structured, and unstructured data from diverse internal and external sources while ensuring data quality, consistency, and reliability.
• Implement, support, and modernize enterprise data integration solutions utilizing platforms such as Informatica, IBM Data Stage, or comparable ETL tools, including migration of legacy ETL processes to modern data engineering frameworks.
• Collaborate with business stakeholders, analysts, architects, and application teams to gather requirements and deliver scalable, high-performance data solutions.
• Enable Business Intelligence, Advanced Analytics, and AI/ML initiatives by preparing, optimizing, governing, and delivering trusted datasets across the organization.
• Monitor, troubleshoot, automate, and optimize data platforms, pipelines, and cloud environments while providing operational support for critical enterprise data integration processes to ensure high availability, performance, scalability, security, and operational excellence.

Basic Qualifications:

• Bachelor's degree, or equivalent work experience
• Three to five years of relevant experience

Preferred Skills/

Experience:

• Strong experience designing and supporting Data Warehouses, Data Lakes, Lakehouse, and modern data architecture, including data integration and large-scale data management solutions.
• Advanced proficiency in SQL with hands-on expertise in data modeling, database design, performance tuning, query optimization, and data engineering best practices.
• Experience developing or supporting data processing, automation, orchestration, and ETL/ELT solutions using Python.
• Hands-on experience with at least one major cloud platform including AWS, Microsoft Azure, or Google Cloud Platform (GCP) and related cloud-native data services.
• Experience supporting data platform modernization initiatives, including migration of data warehouses, ETL/ELT processes, and analytics workloads from on-premises environments to cloud-based architectures.
• Strong understanding of end-to-end ETL and data integration architecture, including data movement, orchestration, transformation, operational support, and performance optimization across multiple technology platforms.
• Experience with modern big data and orchestration frameworks such as Apache Spark, Databricks, Airflow, or similar technologies.
• Strong knowledge of Dev Ops, source control, CI/CD, and version management practices using tools such as Git, Git Hub, Azure Dev Ops, or equivalent platforms.
• Understanding of data governance, data quality, metadata…