Nike Lead Data Engineer - Consumer Product & Innovation 

United States, Oregon, Beaverton 

521363097

Job Summary

Consumer Product and Innovation. In this role, you will be responsible fordesigning, building, and maintaining scalable data pipelines and analytics solutions. As a Lead Data Engineer, you will play a key role in ensuring that our data products are robust and capable of supporting our Advancednalytics andntelligence initiatives.You will be reporting to the Engineering Director and be part of a team that will be a driving force in building a

Key Responsibilities

• Lead the design, development, and deployment of scalable data pipelines and architectures.

• data scientists,engineers, analysts, product managers and business stakeholders to understand data requirements, translate them into technical specifications and deliver data solutions that drive decision-making.

• Mentor and provide technical guidance to junior data engineers, fostering a culture of collaboration, innovation, and continuous improvement.

• Develop and enforce best practices for data engineering, including coding standards, data governance, and performance optimization.

• Communicate complex technical concepts to non-technical stakeholders, ensuring alignment and understanding across teams.

• Participate in code reviews, provide feedback, and contribute to continuous improvement of the team's coding practices.

• Design, build, and maintain robust ETL/ELT pipelines, reusable components, frameworks, and libraries to process data from a variety of data sources ensuring data quality and consistency.

• Monitor and troubleshoot data pipelines, ensuring high availability and performance.

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

Technical Expertise:

• Proven experience (5+ years) as a Data Engineer, with a focus on Databricks,PySpark, and SQL.

• Strong expertise in Apache Spark and distributed computing frameworks, with hands-on experience optimizing Spark jobs for performance and scalability.

• Proficiency in SQL, with the ability to write complex queries and perform data transformations.

• Experience with Databricks LakehousePlatform, Medallionarchitectureand Delta Lake.

• Experience working with AWS including data services such as S3 and RDS.

• Experience with data modeling, ETL/ELT processes, and data warehousing concepts.

• Experience with CI/CD pipelines, version control (Git), and DevOps practices in a data engineering context.

:

• Strong leadership skills with a proven ability to lead and mentor data engineering teams.

• Excellent problem-solving skills and the ability to design solutions for complex data challenges.

• Effective communication and collaboration skills, with the ability to work cross-functionally and translate technical concepts for non-technical stakeholders.

:

• Bachelor Degree or a combination of relevant education, training and experience

Preferred Qualifications:

• Familiarity with real-time data processing frameworks such as Apache Kafka, Kinesis, or similar.

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

• Certification in Databricks (e.g., Databricks Certified Data Engineer, Databricks Certified Developer for Apache Spark).