WHO YOU’LL WORK WITH

A Lead Data Engineer will work in the Data and Artificial Intelligence organisation of Nike and will focus on building highly complex and performant data pipelines that'll drive Nike's data driven strategies for the future of sports.

As a Lead data engineer you will work with your team to build robust data pipelines that are highly efficient and scalable and optimized by following the standards defined by the Principal Engineers.

WHO WE ARE LOOKING FOR

In this role, we are looking for self-driven individuals who have deep technical knowledge in the big data domain. This role requires the individual to be an excellent problem solver who'll design and implement complex data pipelines which solve business problems of Nike. Excellent interpersonal skills and good experience in leading team of engineers and analysts.

The core competencies required for this role include -

• Bachelor’s degree in computer science engineering or equivalent

• 8-12 years of hands-on experience in data engineering field

• In depth big data tech stack knowledge

• Expertise in pyspark and SQL

• Expertise in databricks, snowflake, airflow, any one of the cloud ( AWS , Azure , GCP )

• Excellent written and verbal communication skills

WHAT YOU’LL WORK ON

As a lead data engineer, You will collaborate closely with Architects and principal engineers and the engineering managers to deliver key changes to data pipelines that drive Nike's data strategy

On a day-to-day basis, you'll focus on -

• Building, enhancing, and troubleshooting complex data pipelines

• Oversee the design and build components, frameworks and libraries at scale to support NIKE products

• Review design, code, test plans, and dataset implementation performed by other engineers in support of maintaining NIKE engineering standards

• Anticipate, identify, and troubleshoot complex issues and perform root cause analysis to proactively resolve products and operational issues

• manage the process of building continuous integration, test-driven development and production deployment frameworks

• Collaborating with product managers, engineers, analysts to build, enhance and troubleshoot data pipelines

• Collaborate with senior, lead and principal engineers to define and implement quality standards across data pipelines

• Contribute towards the design and architecture of data pipelines

• Implement data quality and reliability measures across data pipelines