Lead Data Engineer - Consumer Product & Innovation

Job Summary 

We are seeking a highly skilled and motivated Lead Data Engineer to join our data engineering team in Nike’s Consumer Product and Innovation (CP&I) organization. In this role, you will be responsible for designing, 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 Advanced Analytics and Business Intelligence initiatives. You will be reporting to the Engineering Director and be part of a team that will be a driving force in building a cross-capability data foundation, defining and implementing data products to deliver data and AI solutions that drive business growth for Nike. 

Key Responsibilities 

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

• Collaborate with 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. 

Required Qualifications

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 Lakehouse Platform, Medallion architecture and 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. 

Leadership & Collaboration:

• 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. 

 Education:

•  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).