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# Campus Graduate Masters Full-Time Manager - 2027 Data Engineer II, Enterprise Technology Services- Phoenix, AZ

- **Company:** American Express Company
- **Location:** Phoenix, AZ, United States
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Agile Methodology, Data Analysis, Big Data, BigQuery, Cloud Database, Code Review, Information Systems, Databases, Computer Engineering, Continuous Integration, Couchbase Servers, Data Architecture, Data Validation, Data Cleansing, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Structures, Data Systems, IBM DB2, Database Development, Software Debugging, Distributed Systems, Apache HBase, Apache Hive, Python (Programming Language), PostgreSQL, Microsoft SQL Server, MongoDB, Online Analytical Processing, NoSQL, Online Transaction Processing, Oracle (Applications), Performance Tuning, Scrum Methodology, Redis, Cloud Services, Standard Sql, Software Engineering, Enterprise Software Applications, Test-Driven Development (TDD), System Availability, Indexer, Information Technology, Cassandra, Non-relational Database, Data Management, Software Version Control, Data Pipelines, Programming Languages
- **Published:** October 15, 2026
- **Apply:** https://www.dice.com/job-detail/b7b5a066-5c3b-4a7d-a0d7-d1f9b37b881f

## About the Role

* Must have earned a Master's degree in Computer Science, Computer Engineering, Information Systems, Data Engineering, Data Science, Engineering, or another technical field before the full-time start date.
 * Foundational knowledge of SQL and at least one programming language such as Python or Java.
 * Foundational understanding of computer science concepts including data structures, algorithms, debugging, testing, and problem solving.
 * Familiarity with data requirements, data models, data pipelines, database systems, storage formats, and architecture patterns that support business and product goals.
 * Demonstrated interest in data engineering, databases, data platforms, analytics, cloud data tooling, Big Data, or software engineering.
 * Strong communication, collaboration, organization, attention to detail, and learning agility with the ability to work effectively in a team environment.

Preferred Qualifications

 * Students must have a graduation date between December 2026 and June 2027.
 * Knowledge of relational and non-relational database concepts such as data modeling, indexing, partitioning, replication, high availability, encryption, performance tuning, and data maintenance.
 * Familiarity with Big Data, NoSQL, or cloud-native data platforms such as HBase, Hive, MongoDB, Cassandra, Redis, Couchbase, BigQuery, Spanner, PostgreSQL, Oracle, MS SQL Server, DB2, or similar tools.
 * Awareness of distributed systems, multi-tier architectures, scalable storage patterns, online transaction processing, online analytical processing, and production support concepts.
 * Exposure to data modeling, data preparation, ETL, data validation, lineage, documentation, access patterns, and data quality practices.
 * Interest in data preparation practices that support analytics, business intelligence, reporting, operational insights, or data-driven decision-making.
 * Familiarity with Agile, Scrum, Test-Driven Development, CI/CD, version control, code reviews, or related software delivery practices.
 * Awareness of data governance principles, including validation, privacy, lineage, data quality, security, and protection of sensitive information.

## Description

* Support the design, development, testing, and improvement of data pipelines, database infrastructure, and integration patterns.
 * Review data requirements, sources, flows, mappings, and models to help align data solutions with data architecture and business needs.
 * Assist with database development and support across relational, non-relational, NoSQL, Big Data, and cloud-native data platforms.
 * Apply foundational data engineering concepts such as partitioning, indexing, storage patterns, data quality checks, performance tuning, and scalable processing under guidance.
 * Collaborate with product, business, architecture, analytics, and engineering partners in an Agile team environment.
 * Support monitoring, observability, troubleshooting, documentation, data preparation, and continuous improvement activities that strengthen data quality and pipeline reliability.
 * Contribute to data modeling, ETL, data validation, source-to-target documentation, and data maintenance practices.
 * Follow engineering, data governance, privacy, security, and documentation practices to help protect sensitive information and support responsible data use.