-
ID
#41617367 -
Job type
Permanent -
Salary
TBD -
Source
BOEING -
Date
2022-05-23 -
Deadline
2022-07-22
Senior Software Developer - Cloud Data Platform (Virtual)
Washington, Auburn, 98071 Auburn USAPermanent
Vacancy expired!
Job Description
At Boeing, we innovate and collaborate to make the world a better place. From the seabed to outer space, you can contribute to work that matters with a company where diversity, equity and inclusion are shared values. We're committed to fostering an environment for every teammate that's welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us.Boeing Talent Acquisition is seeking Senior Software Developers to join our Cloud Data Analytics Platform Team in Seattle, WA.Boeing's single analytical data plane in Google Cloud Platform serves as the foundation for data analytics capability of the company. The cloud-based infrastructure and self-services enable the company to operate in a more agile environment, responding to and anticipating analytics user and developer needs.We are looking for passionate and talented Senior Software Developers to join us! As a Cloud Data Platform Developer you will have the opportunity to work with cutting-edge cloud architectures and help scale our data-driven platform. This is a growing team, meaning you will help define the culture and processes with us as we build out the team together. If you love designing, developing and running systems and infrastructure that will help analytics developers and users, then this is the place for you!Position Responsibilities:- Build a single Cloud platform architecture for analytics products that simplify and drives the productivity of data scientist platform, data scientists, data analysts, machine learning developers, and analytics solutions developers throughout the company
- Architect, develop and deliver enterprise-wide data infrastructure as a service (including but not limited to domain-agnostic data processing/pipeline tools; data API frameworks; business intelligence tools), enabling data product teams to move fast and with high quality for their stakeholders
- Architect, develop and deliver enterprise-wide machine-learning(ML) as a service, enabling compliant management of ML models while making it fast to get an ML model into production
- Dive deeply into the data and understand the business processes supported, applying statistics and data modeling to gain actionable business insights and drive recommendations
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