Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 3 years of experience coding in one or more programming languages.
- 3 years of experience working with data infrastructure and data models by performing exploratory queries and scripts.
- 3 years of experience designing data pipelines, and dimensional data modeling for synch and asynch system integration and implementation using internal (e.g., Flume, etc.) and external stacks (DataFlow, Spark, etc.).
- 3 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.
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About the job
As a Data Infrastructure Manager for the Global YouTube Marketing team, you will be a key driver in shaping our marketing strategy through the development and maintenance of a robust data infrastructure. You will build, mentor and lead a team of talented data engineers, empowering them to build cutting-edge data solutions, design insightful dashboards, and establish robust data pipelines that fuel our marketing efforts. In this role, you will enable the marketing team to make data-driven decisions that optimize campaigns and quantify the incremental impact of marketing initiatives.
YouTube/Video Global Solutions is the link between Google video products and sales. Our mission is to fuel innovation that keeps YouTube and Video free and accessible to the world. We do this by translating global market needs into meaningful product solutions that drive business results for content partners and customers.
Responsibilities
- Design and build data processing systems with a particular emphasis on security, compliance, scalability, efficiency, reliability, and portability.
- Create or consult in creating data visualizations using Business Intelligence (BI) tools (e.g., PLX, Datastudio, Tableau, etc.).
- Develop and maintain data models, pipelines, and exchange formats to assist in the visualization, analysis, and interpretation of data and for use of data in ML training/models.
- Provide ongoing support for data users through maintenance of reports, queries, and dashboards, fielding user questions, authoring documentation, and delivering training.
- Collaborate with cross-functional teams (e.g., Marketing, Data Science, Engineering, and Analytics) to understand data requirements and deliver impactful data solutions.