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# Data Engineer-Senior II

- **Company:** FedEx
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Information Engineering, Data Governance, Document-Oriented Databases, Information Technology
- **Published:** August 26, 2026
- **Apply:** https://fedex.paradox.ai/co/FederalExpressCorporation41/Job?job_id=P25-334629-2&posting_type=1

## About the Role

Education: Bachelors' degree or equivalent in Computer Science, MIS, or similar discipline.
Accreditation: Specific business accreditation for Business Intelligence.

Experience: Relevant work experience in data engineering based on the following number of years:
Associate: Prior experience not required
Standard I: Two (2) years
Standard II: Three (3) years
Senior I: Four (4) years
Senior II: Five (5) years

Knowledge, Skills and Abilities

 * Fluency in English
 * Analytical Skills
 * Accuracy & Attention to Detail
 * Numerical Skills
 * Planning & Organizing Skills
 * Presentation Skills

## Description

Responsible for designing and implementing data pipelines, ensuring data quality and accessibility, creating data models, managing ETL processes, optimizing databases, collaborating with stakeholders, utilizing big data tools, implementing real-time data processing, ensuring data governance and security, documenting data architecture, and delivering data-driven insights.

 1. Design and implement data pipelines to collect, process, and store data efficiently.
 2. Ensure data quality, integrity, and accessibility.
 3. Create data models to support business analytics and reporting.
 4. Work with data scientists, analysts, and other stakeholders to understand data needs, and design schemas.
 5. Build and manage ETL (Extract, Transform, Load) processes for data integration and automate data ingestion and transformation tasks.
 6. Administer and optimize databases (both SQL and NoSQL). Monitor database performance and troubleshoot issues.
 7. Collaborate with software engineers to integrate data systems with applications.
 8. Utilize big data tools for processing large datasets, such as Hadoop or Spark.
 9. Implement distributed systems for real-time data processing.
 10. Ensure data governance and security through compliance with data governance policies and data security standards.
 11. Implement data access controls and encryption techniques.
 12. Document data architecture, pipelines, and processes.
 13. Provide reports and insights based on data analysis.


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