Data Engineer

Purpose & Overall Relevance For The Organization

Earlier this year, adidas decided to elevate the Data & Analytics function, moving from a tech service to a value-oriented business function that actively and directly impacts adidas’ P&L.

The Data & Analytics function will include a Brand Advanced Analytics team, which will work hand in hand with the Global Operations organization on strategically relevant use cases that have the potential to deliver substantial value and redefine the way that the Brand business function operates.

For example, the Brand Advanced Analytics team will build data science products to ensure our range is optimized, to build prediction models for our key franchises and Hype drops, to establish best in class pricing models amongst others.

Key Responsibilities

• Data Engineering
• Build data pipelines to support data science projects following data engineering best practices
• Data exploration, quality checks & implement exception handling process
• Identify, design, and implement internal operational improvements: automating manual processes, optimizing data delivery, improving data quality, etc
• Work with data scientists and analysts to productionize data pipelines and machine learning models, so that they can scale and accommodate various business requirements
• Drive collaborative reviews of design, code, test plans and dataset implementation performed by other data engineers in support of maintaining data engineering standards
• Ensure information security standards are maintained at all time
  
  

• Stakeholder management
• Partner with the Global Operations business owners, project teams and colleagues at all levels, ensuring their views and requirements are captured
• Act as the ambassador for the data product, showcasing the key functionalities, driving adoption, and assuring operational excellence together with IT peers
  

Key relationships

• Global Operations project teams
• Advanced Analytics Teams
• Other teams within Data & Analytics (e.g., Data assets, data platforms, data governance, market teams)
  
  

Requisite Education And Experience / Minimum Qualifications

• Education & Professional experience
• Bachelor’s degree, preferably in computer science (or the equivalent)
• 4 years’ hands-on experience in developing scalable Big Data applications or solutions on distributed platforms, experience with supply chain and operations data is a plus
  
  

• Hard skills
• Experience in building data engineering capabilities with DataBricks, including data pipelines, monitoring & troubleshooting.
• Experience with Continuous Integration and Automated Test tools such as Jenkins, Git & Docker
• Experience working with Spark, Python, Pytest
• Strong experience in modular code development using IDE
• Experience working in Agile and Scrum development process.
  
  

• Soft skills & attitude
• Good written and oral communication skills (English)
• Comfortable in presenting complex topics.
• Proven team player who can collaborate across functions and organizations.
• High resilience and solution-oriented attitude
• Excited to work as part of a data science team and embark on a path of steep skill growth.