Sr Engineering Manager - Data Engineering
Posted: January 13, 2017
Reference ID: 1797420438
The Analytics Platform & Application (APA) team at LinkedIn is responsible for building an interactive, self-serve and scalable platform that enables engineers, product managers, business analysts, data scientists and other stakeholders to analyze both data at rest and data in motion in real time. We are looking for a Sr. Engineering Manager to lead the team responsible for building data solutions for the enterprise.
• Build the next-generation data warehouse, and highly scalable data pipelines that feed LinkedIn's site-facing data products as well as internal reporting and analytics, leveraging the latest technologies (Hadoop, Presto, Spark, . . . ). • Lead and manage engineering team(s) with a passion for mentoring and growing top engineering talent. • Drive execution of data solution and technology roadmap by collaborating with cross functional teams, products, analytics, engineering, siteops, and machine learning experts. • Provide technical leadership, driving and performing best engineering practices to initiate, plan, and execute large-scale, cross functional, and company-wise critical programs. • Identify, leverage, and successfully evangelize opportunities to improve engineering productivity.
• Create a collaborative work environment that fosters autonomy, transparency, innovation and learning, while holding a high bar for craftsmanship.
• B. . /B. . Degree or higher in Computer Science or related technical discipline, or 5+ years of equivalent practical experience.
• Strong management skills for planning and executing complex multi-team projects • Strong communication skills and customer focus • Experience building and running in production (24x7 environments) distributed large scale data management systems, such as metadata management, scheduler, workflow system. • Experience E2E in big data space (Hadoop, Pig, Hive, M/R, Java) experience. • Ability to recruit for and manage technical teams. • Ability to work with geographically distributed teams • Knowledge and experience of working with machine learning systems will be a plus.