Lead DevOps Engineer
Nike
NIKE, Inc. does more than outfit the world’s best athletes. It is a place to explore potential, obliterate boundaries and push out the edges of what can be. The company looks for people who can grow, think, dream and create. Its culture thrives by embracing diversity and rewarding imagination. The brand seeks achievers, leaders and visionaries. At Nike, it’s about each person bringing skills and passion to a challenging and constantly evolving game.WHO ARE WE LOOKING FORWe seek passionate Lead DevOps Engineer to build automation to enable machine learning engineers to continuously deliver predictive analytics for forecasting, personalization, and inventory optimization. As a Lead DevOps Engineer, you will be expected to own projects end-to-end – from conception to operationalization, demonstrating an understanding of the full software development lifecycle. As a result, you will be expected to provide technical vision and guidance to your teammates; therefore, strong communication skills are critical in this role.WHAT YOU BRING TO NIKEProfessional experience in DevOpsNice to have: software engineering, data engineering, or related field experienceExperience with building CI/CD infrastructure (preferable Jenkins)Experience with Docker, Kubernetes, and related tools and systemsExperience with AWS components and services, particularly, IAM, ECS/EKS, EMR, S3, and Lambda/ServerlessExperience with Infrastructure as a Code, especially TerraformStrong Python, shell scripting, and SQLExperience with technologies like Apache NiFi, Apache Airflow, Postgres, or Redis would be an advantageFamiliar with principles of solution architecture and technical designEffective communication skills (with team members, the business, and in code)We are looking for candidates who enjoy a collaborative and academic environment where we develop and share new skills, mentor, and contribute knowledge and software back to the analytics and engineering communities both within Nike and at-large.WHAT WILL YOU WORK ONIf this is you, you’ll be working with the Data and Artificial Intelligence team at Nike. With teammates in Portland, Boston, China, and Poland, you’ll be joining a global organization working to solve machine learning problems at scale. You’ll be designing and implementing automation for scalable applications that leverage prediction, image recognition and natural language processing models to deliver data driven decisions that result in immense business impact. You’ll also contribute to core advanced analytics and machine learning platforms and tools to enable both prediction and optimization model development.
WHO WILL YOU WORK WITHAIML is one of the key groups within Data and Artificial Intelligence. We’re chartered to scale machine learning and AI at Nike. For areas of the business early in their analytics journey, we embed cross-disciplinary teams of data scientists and engineers to unlock new capabilities and answer unsolved (or unasked!) questions.In mature areas with preexisting data science teams, we help scale machine learning by attaching engineering squads to grow their capacity to deliver for the business. In addition, we collaborate closely with platform and architecture partners to develop capabilities that simplify machine learning at scale within Nike (e.g. model deployment, A/B testing, model monitoring).NIKE, Inc. is a growth company that looks for team members to grow with it. Nike offers a generous total rewards package, casual work environment, a diverse and inclusive culture, and an electric atmosphere for professional development. No matter the location, or the role, every Nike employee shares one galvanizing mission: To bring inspiration and innovation to every athlete* in the world.NIKE, Inc. is committed to employing a diverse workforce. Qualified applicants will receive consideration without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity, gender expression, veteran status, or disability.#LI-EMEA Gdańsk, pomorskie
Wed, 04 Dec 2024 02:01:01 GMT
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