Lead Data Engineer

About the role

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.

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.

Responsibilities

• 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

Requirements

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)