Google Hiring Data Engineer | Analytics and Data Science – YouTube

Qualifications

Minimum qualifications:

• Bachelor’s degree in a quantitative field (e.g., Statistics, Computer Science, Math, Physics, Engineering), or equivalent practical experience.
• 1 year of experience in data engineering or business intelligence roles.
• Experience with relational databases, including SQL queries, database definition, and schema design.
• Experience with one or more programming languages (e.g., Python, Java, C++, etc.).

Preferred qualifications:

• Master’s degree in a quantitative field (e.g., Computer Science, Engineering, Statistics, Math).
• Experience with data warehouses, distributed data platforms, and data lakes.
• Ability to navigate ambiguity and work in a fast-moving environment with multiple stakeholders.
• Excellent business and technical communication, organizational, and problem-solving skills.
• Excellent structured thinking skills, with the ability to break down multi-dimensional problems.

Job Description & Responsibilities:-

As a Data Engineer within YouTube Analytics and Data Science, you will be part of a community of analytics professionals who work on projects ranging from developing data pipelines that help run the business, and build tools to analyze the content partnerships and creator ecosystem that guide business leadership on the effectiveness of partner facing business teams.

• The team uses SQL and YouTube’s ETL systems to produce useful datasets, establish best practices for data sets and reporting, and develop expertise in various data domains.
• Conduct requirements gathering and project scoping sessions with subject matter experts, business users, and executive stakeholders to discover and define business data needs.
• Design, build, and optimize the data architecture and extract, transform, and load (ETL) pipelines to make them accessible for Business Data Analysts, Data Scientists, and business users.
• Work with analysts to scale value-creating capabilities, including data integrations and transformations, model features, as well as statistical and machine learning models.
• Drive standards in data reliability, data integrity, and data governance, enabling accurate, consistent, and trustworthy data sets, business intelligence products, and analyses.
• Engage with the analyst community, communicate with analysts to understand critical user journeys and data sourcing inefficiencies, advocate best practices, and lead analyst trainings.