Data Engineer Google Jobs in New York (NOW HIRING)

Minimum qualifications:

• Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience.
• 10 years of experience with network routing protocols, design and troubleshooting, with network equipment providers.
• Experience with planning and designing networks (e.g., multiple sites, multiple products, etc.).

Preferred qualifications:

• 5 years of experience with developing performance infrastructure, distributed systems, or experience with compute technologies, storage or hardware architecture.
• Experience in one or more programming languages such as C , Go, or Python.
• Experience with data center Networking with in-depth knowledge of routing protocols and overlay networks such as EVPN-VXLAN and IPv6.
• Proficiency in coding, scripting, networking system health, diagnosis and resolution, network and software testing.

About the job
Google is proud to boast a network that provides service to millions of Internet users around the world. The Network Engineering team is responsible for operating that network reliably and at scale. As a member of the team, you have a direct impact on design and feature enhancements to keep our systems running smoothly. You also ensure that network operations are safe and efficient by monitoring network performance, coordinating planned maintenance, adjusting hardware components and responding to network connectivity issues. Google's complex network generates a constant stream of challenges which require you to continually be innovative with an evolving set of technologies. Keeping the network reliable ensures that our users stay connected with our suite of applications, products and services.
Google Distributed Cloud (GDC) is designed as a converged hardware and software solution for customers, i.e. Public Sector, Enterprise, Telco, Retail. GDC includes dedicated hardware, network fabric, operating systems, and a curated set of Google Cloud services, providing a consistent cloud experience in tightly controlled environments. GDC network fabric consists of multiple layers including physical network, overlay network, host and Kubernetes network.
In this role, you will support the most stringent data residency, security, compliance, and privacy requirements, operating completely disconnected from Google Cloud.
GDC connected (GDCc) is a hardware and software product that brings Google Cloud to customer edge environments without compromising on latency or connectivity.
The AI and Infrastructure team is redefining what's possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide.
We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $176000 - $256000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities

• Drive impact for GDC across a combination of large project leadership, or solving very deep and ambiguous technical problems.
• Provide deep insight and efficient resolution of complex operational issues that may arise in production, and would then follow that up with the design and development of processes that make sure that those issues don't re-occur.
• Anticipate scaling bottlenecks, software inefficiencies or shifts in usage, improve network planning and quantitative models, simplify and standardize existing solutions, increase availability and reliability, or make data-driven optimizations and adjustments.
• Coordinate or lead efforts involving multiple teams organizations across multiple medium-large projects in a collaborative manner.
• Drive significant improvements in performance of a team, through leadership, mentorship, and application of good engineering principles.

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