AI Cloud Data Engineer

Overview

UMB's Artificial Intelligence (AI) team drives automation, data-driven decision making and software modernization using AI-centric tools. As AI Cloud Data Engineer, you will design, build, and optimize cloud-based data pipelines and platforms that support enterprise AI/ML workloads. This role combines cloud engineering and data engineering to deliver secure, scalable, and compliant data solutions in hybrid and multi-cloud environments.

• Design and develop cloud-native data pipelines to support AI/ML training, validation, and inference.
• Build and optimize data lakes, warehouses, and lakehouse environments (e.g., Delta Lake, Apache Iceberg) in the cloud.
• Collaborate with AI Data Architect and AI Cloud Architect to ensure data platforms meet enterprise standards.
• Automate ETL/ELT workflows and orchestration using tools such as Airflow, Glue, and Dataflow.
• Ensure compliance with financial data security, governance, and regulatory requirements (GLBA, GDPR, OCC, FFIEC).
• Integrate structured, unstructured, and streaming data sources from banking systems, payment hubs, and customer channels.
• Implement monitoring and observability solutions for cloud-based data pipelines.
• Optimize data workflows for performance, scalability, and cost efficiency in cloud environments.
• Support disaster recovery, resiliency, and data availability strategies for AI platforms.
• Troubleshoot data pipeline, integration, and cloud deployment issues.

• Bachelor's degree in Computer Science, Data Engineering, Cloud Computing, or related field and at least 3 years of experience in cloud data engineering or enterprise data integration, OR equivalent combination of education and work experience.
• Proficiency with at least one major cloud platform (AWS, Azure, GCP).
• Experience with SQL and programming languages such as Python, Scala, or Java.
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Expertise in data pipeline frameworks (Apache Spark, Kafka, Flink, Dataflow).
• Knowledge of cloud-native data services (AWS Redshift, Glue, Snowflake, Big Query, Azure Synapse).
• Familiarity with MLOps and integrating data workflows with AI model lifecycle.
• Strong understanding of data governance, lineage, and metadata management.
• Ability to design fault-tolerant, secure, and scalable data workflows in the cloud.

• Master s degree in Data Science, Cloud Engineering, or related discipline and at least 5 years of experience in cloud data engineering within financial services or regulated industries.
• Certifications:
  
  AWS Certified Data Analytics Specialty, Google Professional Data Engineer, or Microsoft Azure Data Engineer Associate.
• Experience with regulatory reporting and AI-ready data pipelines.
• Background in high-volume financial data systems or real-time payments.
• Hands-on experience with Lakehouse technologies (Delta Lake, Apache Iceberg, Hudi).
• Knowledge of distributed data architectures and data mesh patterns.
• Familiarity with AI observability tools and data drift monitoring.
• Experience with privacy-preserving technologies and secure data-sharing frameworks.
• Deep understanding of cloud cost optimization for data-intensive AI workloads.

Applicants must have legal authority to work in the United States. Work Visa sponsorship is not available for this position.

Final date to receive applications:
March 8, 2026

Compensation Range: $69,230.00 - $