Data Engineer, Trust and Safety
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Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience designing data pipelines, and dimensional data modeling for synchronous and asynchronous system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).
- 5 years of experience coding in one or more programming languages (e.g., SQL and Python).
- 5 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
Preferred qualifications:
- 5 years of experience with statistical methodology and data consumption tools such as business intelligence tools, collabs, jupyter notebooks, Tableau, Power BI, DataStudio, and business intelligence platforms.
- 3 years of experience developing project plans and delivering projects on time within budget and scope.
- 3 years of experience partnering with stakeholders (e.g., users, partners, customer), and managing stakeholders, or customers.
- Experience with Machine Learning (ML) for production workflows.
About the job
Global Solutions is the link between Google products and business. Your focus is to help turn innovation into complete, packaged tools that allow our customers to get the most that they need from our products. As part of the Global Solutions team, you'll help to ensure that Google has adopted the right strategy for our products. Working in one of three specialized areas – Performance, Brand, or Publisher & Platform – you'll use your expertise to help front-line Sales partners to work their magic quickly and effectively. You are passionate about all things digital, and want to help shape the world of online advertising.Responsibilities
- Acquire an understanding of business processes, tools and customer expectations, design and develop scalable data infrastructure to support reporting needs.
- Design, implement, test, optimize and troubleshoot analytics and report solutions to solve business performance, management challenges as well as to meet regulatory report requirements.
- Collaborate with and influence business and engineering stakeholders to ensure the data infrastructure and products meets constantly evolving requirements.
- Work with analysts to productionize analytics and reporting prototypes, and various statistical and Machine Learning (ML) models.
- Design, implement and own technical implementation of production-level data pipelines, documentation, check-in process, etc. Write and review technical documents, including design, requirements, and process documentation.
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