Director, Data Engineering

Key Skills

Java Programming, SAP, Python Programming, Database, iOS platform, SQL, DevOps, AWS, Business Intelligence, Data Modeling, NoSQL, QlikView, Tableau, Snowflake, Teradata, Amazon Athena, Data Analytics, MySQL, Amazon RedShift, DynamoDB, Lambda, Objective-C, Data Architecture

Job Description

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, Inc. it’s about each person bringing skills and passion to a challenging and constantly evolving game.

Define technical roadmaps and critical data platform and analytics deliverables. Lead a technical team to develop, mentor and continuously elevate the team’s engineering skills and to deliver innovative and high-quality solutions. Build distributed, cloud scalable, and high performance data lake solutions while working with Nike functional business teams, commercial analytics, and EDA architecture teams. Define new analytics engineering technical requirements, solution architecture, design and development on various next generation analytics data platform tools. Provide technical leadership and oversight over various technical product teams to develop, deploy, and support new analytics engineering solutions to production using common Nike technologies.

Applicant must have a Bachelor’s degree in Computer Science, Computer Information Systems, or Mechanical Engineering and 5 years of progressive post-baccalaureate experience in the job offered or a related occupation. Experience must include:

• Data Modeling and Data Architecture;
• Data Analytics and Visualization;
• Data product management;
• DevOps practices;
• Agile methodologies;
• Team Management;
• Strategic visioning and budget ownership;
• Portfolio management;
• Relational, NoSQL databases, such as, DynamoDB;
• AWS, such as, Lambda, EC2, EMR, S3, SQS, Athena, and Kinesis or similar cloud platform;
• Data Warehousing, such as, AWS Redshift, Snowflake, and Teradata;
• Data Visualization Platform, such as, QlikView, and Tableau;
• SAP Business Intelligence and SAP Business Objects;
• SQL and MySQL