Intern- Data Analytics Engineer
Indexed description
ETO is committed to helping our employees grow, develop and advance in their career. Our Workforce of the Future, DEI and Upskilling initiatives allow you to network across the organization, volunteer in our community, and build your technical and soft skills. We believe that investing in your success and well-being is an investment in our customers and our business.
Together we are building a culture that values diversity, celebrates growth and creates a space of belonging for all our team members. Our people are what set us apart and make us great.
Zions Enterprise Data & Analytics is looking for a Data Analytics Engineering Intern.
The intern will assist the Enterprise Data Warehouse function to acquire, process, and analyze data from Enterprise Data Warehouse, Data Lake, and other data repositories to support financial or analytical reporting.
The Data Analyst Intern will:
- Apply business analytics to define, identify, and obtain source data from various sources such as Enterprise Data Warehouse, Data Lake, and other enterprise platforms; analyze, map, transform, and validate data to support Financial Data Management reporting.
- Leverage AI-enabled technologies to enhance data discovery, documentation, metadata management, and analytical productivity. Evaluate opportunities to develop and utilize Model Context Protocol (MCP) services and integrations to provide secure, reusable access to enterprise data and business knowledge.
- Develop and automate data quality validations, reconciliation processes, and business rule checks to improve confidence in enterprise data assets and reporting outcomes.
- Contribute to CI/CD processes by integrating automated data validations, testing, and deployment controls into enterprise data workflows to improve reliability, scalability, and maintainability.
- Assist in monitoring and improving data observability, data quality, lineage, and pipeline performance to proactively identify and address data issues.
- Working toward a Bachelor's or Master's degree in Information Systems, Data Analytics, Computer Science, Economics, Mathematics, Statistics, Engineering, or a related technical discipline.
- Enjoy analyzing, modeling, and interpreting large and complex datasets to solve business problems.
- Experience with relational databases, data warehousing concepts, and writing SQL queries.
- Exposure to source control systems such as Git and software development best practices.
- Familiarity with CI/CD concepts, automated testing frameworks, and deployment pipelines.
- Exposure to modern data transformation and analytics engineering tools (e.g., dbt) is a plus.
- Interest in Artificial Intelligence (AI), Generative AI, Large Language Models (LLMs), AI agents, and Model Context Protocol (MCP) frameworks.
- Experience or coursework involving API integrations, workflow automation, or cloud-based data platforms is a plus.
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