Data Engineer 4 Remote

Title: Data Engineer 4

Location: Remote, US

Duration: 12-month contract

NIKE, Inc. does more than outfit the world's best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At Nike, it’s about each person bringing skills and passion to a challenging and constantly evolving game.

WHAT YOU WILL DO

As a Lead Data Engineer, you will establish and maintain database management systems, standards, guidelines, and quality assurance processes. You’ll be responsible for key deliverables such as conceptual design, logical database structures, capacity planning, data loading and maintenance plans, external data interface specifications, and security policies. You will document and communicate database designs, evaluate and install database management systems, and code complex programs. You’ll also assist in designing user interfaces and business application prototypes, participate in quality assurance, and develop test application code in a client-server environment.

WHAT YOU WILL WORK ON

• Building scalable and efficient data pipelines
• Data modeling and relational database design
• Data analysis and optimization of storage organization
• Implementing CI/CD and DevOps practices for deploying and supporting software in production
• Working with cloud services to build integrated applications (AWS: EC2, ECS, API Gateway, Lambda)
• Using tools and technologies such as Databricks, Python, Airflow, AWS EMR, and SQL

WHAT YOU WILL BRING

• Bachelor’s degree
• Minimum of 8 years of experience in data engineering
• Strong skills in Python, SQL, and data engineering best practices
• Experience with cloud-based data solutions and production-level deployments
• Ability to adapt business requirements into database specifications and relational models
• Expertise in evaluating data appropriateness for storage and designing optimal storage structures