Data Engineer, Global Sales Activation, Go-To-Market - Google
Mountain View, CA
About the Job
This role may also be located in our Playa Vista, CA campus.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Atlanta, GA, USA; Chicago, IL, USA; New York, NY, USA; Los Angeles, CA, USA; San Francisco, CA, USA; Washington D.C., DC, USA.Minimum qualifications:
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 3 years of experience coding with one or more programming languages (e.g., Python, Java, C/C++).
- 3 years of experience designing and building data pipelines (ETL) and model data, for synch and asynch system integration and implementation.
- 3 years of experience analyzing data, database query (e.g. SQL), and creating dashboards/reports.
Preferred qualifications:
- Master's degree in Engineering, Computer Science, or a related field.
- 3 years of experience partnering with stakeholders (e.g., users, partners, customers).
- 3 years of experience developing project plans and delivering projects on time within budget and scope.
- 3 years of experience designing data models and data warehouses, including data processing automation, data quality, data governance, business intelligence, and data privacy.
- Experience writing and maintaining ETLs which operate on a variety of structured and unstructured sources.
- Structured thinking with the ability to break down complex, ambiguous problems and propose solutions through impactful data modeling designs.
About the job
The Governance, Infrastructure, and Operations (GIO) team sits within Product Operations in the Global Sales Activation (GSA) organization, and is responsible for leading sales workflow activation strategy across business operations, governance, legal compliance, sales enablement, and measurement and analytics. GSA resides within the Go-To-Market Operations (GTM) team and enables Google’s Business Organization (GBO) to drive customer success. Experts in driving process improvements and consistency, team members are analytical and strategic with a pragmatic sense of getting things done.
Data is the fundamental building block for every tool and every insight. We build the data sets that help run the business, piping the relevant data into and out of our tools, and making it useful for analysts across the organization to drive reporting and insights. We are responsible for democratizing GBO and Ads data, helping business leaders make sense of business operations through timely, accurate, and robust business intelligence. We use SQL and ETL systems to produce useful datasets, establish best practices for data sets and reporting, and develop a breadth of expertise in various data domains. Ultimately, Data Engineers scale centralized reporting and automate data processes to ensure the business is operating more effectively and efficiently.
The US base salary range for this full-time position is $118,000-$174,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.Responsibilities
- Centralize ads and customer data to enable discovery and analysis for users.
- Recommend and adopt best practices in developing and modifying existing data models and ETL pipelines. Be responsible for data governance, data integrity, test design, validation.
- Create Extract, Transform, and Load (ETLs) and reporting systems for new data using a variety of traditional as well as large-scale distributed data systems.
- Work closely with analysts to productionize various statistical and machine learning models using data processing pipelines.
- Write and review technical documents including design, development, and revision documents.
