Lead Data Engineer

Position Type

Full Time

Career area

Data

Location

1 Bowerman Drive, Beaverton, Oregon 97005, United States

Job ID

R-88272

Become a Part of the NIKE, Inc. Team

NIKE, Inc. does more than outfit the world’s best athletes. It’s a place where passionate individuals come together to create the future of sport. We are unapologetic about who we are and what we’re after - bringing innovation and inspiration to every athlete in the world. We look for athletes who can push boundaries, elevate our potential and continue leading us to greatness. The next tastemakers, playmakers, risk takers and glue players. Are you game?

LEAD, DATA ENGINEER - NIKE

[Beaverton, OR - USA]

WHO YOU'LL WORK WITH

Consumer Product and Innovation (CP&I) Data and Analytics Engineering sits at the intersection of product innovation and enterprise data strategy at Nike. Reporting to the Engineering Director, this team partners with data scientists, engineers, analysts, and product managers to build a cross-capability data foundation and a semantic layer that powers Advanced Analytics, Business Intelligence, and AI solutions driving business growth.

WHO WE ARE LOOKING FOR

We're looking for a Lead Data Engineer to design, build, and maintain scalable data pipelines and analytics solutions within Nike's CP&I organization. This role drives the development of robust data products that support Business Intelligence and AI initiatives across the business. The candidate will lead technical design and development while mentoring junior engineers and setting standards for data governance, performance, and engineering excellence.

We're seeking someone with deep expertise in distributed data processing and cloud-based data platforms who can translate complex business requirements into reliable, production-grade solutions. The ideal candidate brings strong leadership instincts, excels in cross-functional collaboration, and communicates technical concepts clearly to both engineering peers and non-technical stakeholders. Success in this role requires a builder's mindset, a commitment to continuous improvement, and the ability to thrive in a fast-paced environment where data directly fuels innovation and growth.

Bachelor's degree or equivalent combination of education, experience, or training

8+ years of experience as a Data Engineer with strong expertise in Databricks, PySpark, SQL, and Apache Spark

Hands-on experience with the Databricks Lakehouse Platform, Medallion architecture, Delta Lake, and AWS data services (S3, RDS)

Proven experience leading and mentoring data engineering teams, with strong skills in CI/CD, Git, and DevOps practices

Experience with data modeling, ETL/ELT processes, real-time data processing frameworks (Kafka, Kinesis, or similar), and cross-functional stakeholder communication

Preferred qualifications:

Knowledge of Generative AI and Machine Learning pipelines and integrating them into production environments

Databricks certification (e.g., Databricks Certified Data Engineer or Databricks Certified Developer for Apache Spark)

WHAT YOU'LL WORK ON

You'll be at the forefront of building Nike's data foundation and semantic layer - designing and delivering the pipelines, frameworks, and data products that turn raw data into insights shaping product innovation and business strategy. This is hands-on, high-impact work at the intersection of engineering craft and enterprise scale.

Lead the design, development, and deployment of scalable data pipelines and architectures that power analytics and AI initiatives across CP&I

Partner with data scientists, analysts, product managers, and business stakeholders to translate requirements into technical specifications and deliver solutions that drive decision-making

Mentor junior data engineers and champion best practices in coding standards, data governance, and performance optimization

Build and maintain robust, reusable data engineering components, frameworks, and libraries that process data from diverse sources with consistency and quality

Monitor, troubleshoot, and optimize data pipelines to ensure high availability, performance, and reliability at enterprise scale

Implement CI/CD pipelines to automate deployment and testing of data engineering workflows

Participate in code reviews and contribute to a culture of collaboration, innovation, and continuous improvement

We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form.

NIKE, Inc. is a growth company that looks for team members to grow with it. Nike offers a generous total rewards package, casual work environment, a diverse and inclusive culture, and an electric atmosphere for professional development. No matter the location, or the role, every Nike employee shares one galvanizing mission: To bring inspiration and innovation to every athlete in the world.

What You Can Expect

OUR HIRING GAME PLAN

01 Apply

Our teams are made up of diverse skillsets, knowledge bases, inputs, ideas and backgrounds. We want you to find your fit – review job descriptions, departments and teams to discover the role for you.

02 Meet a Recruiter or Take an Assessment

If selected for a corporate role, a recruiter will reach out to start your interview process and be your main contact throughout the process. For retail roles, you’ll complete an interactive assessment that includes a chat and quizzes and takes about 10-20 minutes to complete. No matter the role, we want to learn about you – the whole you – so don’t shy away from how you approach world-class service and what makes you unique.

03 Interview

Go into this stage confident by doing your research, understanding what we are looking for and being prepared for questions that are set up to learn more about you, and your background.