Data Engineer, Global YouTube Marketing
Job Summary
Job Description
As a Data Engineer on the Global YouTube Marketing team, you'll build and maintain reliable data pipelines, contributing to insightful dashboards that support marketing efforts. You'll enable data-informed decisions to optimize campaigns and understand their impact. Responsibilities include designing and building data processing systems emphasizing security, scalability, and efficiency; creating data visualizations using BI tools; developing and maintaining data models and pipelines; providing ongoing support to data users; and collaborating with cross-functional teams. The role requires strong programming skills, experience with data infrastructure and modeling, and expertise in data pipeline design.
Must have:
- Bachelor's degree or equivalent experience
- 3+ years coding experience
- 3+ years data infrastructure experience
- 3+ years data pipeline design experience
- Data visualization using BI tools
- Collaboration with cross-functional teams
Good to have:
- Experience with statistical methodology
- Experience with data consumption tools (Tableau, Power BI, etc.)
- Stakeholder management experience
- Project management experience
- Machine Learning experience
- Understanding of incrementality measurement
8 skills required
Job Details
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 (e.g., DataFlow, Spark, etc.).
Preferred qualifications:
- 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.
- 3 years of experience partnering with stakeholders (e.g., users, partners, customers), and managing stakeholders/customers.
- 3 years of experience developing project plans and delivering projects on time within budget and scope.
- Experience with Machine Learning for production workflows.
- Understanding of incrementality measurement methodologies and frameworks.
About the job
As a Data Engineer on the Global YouTube Marketing team, you will contribute to shaping our marketing strategy by building and maintaining reliable data pipelines. You will work within a team of talented engineers to develop data solutions and contribute to insightful dashboards that support our marketing efforts. In this role, you will help enable the marketing team to make data-informed decisions to optimize campaigns and understand the impact of marketing initiatives.Know the user. Know the magic. Connect the two. At its core, marketing at Google starts with technology and ends with the user, bringing both together in unconventional ways. Our job is to demonstrate how Google's products solve the world's problems--from the everyday to the epic, from the mundane to the monumental. And we approach marketing in a way that only Google can--changing the game, redefining the medium, making the user the priority, and ultimately, letting the technology speak for itself.
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., 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 machine learning 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.
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