Lead Data Engineer

FashionUnited; Beaverton (OR); On-site; USD 150,000 - 210,000; Full time

Job summary

Nike is seeking a Lead Data Engineer for its CP&I Data and Analytics Engineering team in Beaverton, OR. You will design, build, and maintain scalable data pipelines and analytics solutions powering BI and AI initiatives across the business.

You will lead technical design, mentor engineers, and drive data governance and engineering excellence in a fast-paced environment. The role requires deep distributed data processing expertise, cloud platforms, and strong collaboration with data

Qualifications

• 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.

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

Skills

Databricks

PySpark

SQL

Apache Spark

CI/CD

Git

DevOps

Data modeling

ETL/ELT

Real-time data processing

Cross-functional collaboration

Leadership

Education

Bachelor's degree

Tools

Databricks Lakehouse Platform

Medallion architecture

Delta Lake

AWS (S3, RDS)

Kafka

Kinesis

Job description

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