Manager, Data Analysis and Engineering
Santa Clara Valley (Cupertino), California, United States
Software and Services
The Engineering Productivity & Quality Tools team's primary goal is to build internal tools and services for the Software Engineer division at Apple. This team's focus is to improve productivity for our engineering audience in their day-to-day workflows. We need an extraordinary manager to lead our analytics and data science team. This is a newly established area, so if you are looking for an exciting challenge to both grow and manage a team while also improving tools and practices using a data driven approach, then this is the right opportunity for you.
- Management experience in one of the following: Web Development, Data Analysis, Data engineering, Data Science or equivalent
- Deep Understanding of Software Development Practices
- Understanding and execution experience in Machine Learning techniques
- Passion for Continuous Integration and Continuous Delivery Practices
- Change Management Experience
- Experience with Spark, Hadoop, etc.
- Experience with large scale distributed systems
- Strong Understanding of Python, Java, etc.
- Experience working in customer service role
- Excellent Interpersonal Skills
- Ability to develop talented engineers
In this fast paced environment, you will need to be self-motivated and bring a strong background in leading software engineers. Your focus will be on enabling the team to come up with innovative and creative approaches to use our data. This allows our engineers to introduce productivity gains and overall insights and will help craft our roadmap and improve the experience for our internal customers. You will be a strong advocate and evangelist for the people and efforts on your team, helping draw connections for broader applications and focus areas. You will inspire change management efforts in our organization to ensure the data is structured and organized to support the efforts of the team.
Education & Experience
BS/MS/PhD in Computer Science, Computer Engineering, Machine Learning, Data Science, Mathematics or equivalent