About the job

As a Data Engineer for the Analytics, Insights and Measurement (AIM) team, you will Help customers, grow their businesses through trusted analytics, insights and measurement that ensure user privacy.

Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.

Responsibilities

• Create and deliver recommendations, tutorials, blog articles, sample code, and technical presentations, tailoring approach and messaging to varied levels of business and technical stakeholders.

• Design, develop, and maintain reliable data pipelines to collect, process, and store data from various data sources.

• Implement data quality checks and monitoring to ensure data accuracy and integrity.

• Collaborate with cross-functional teams (data science, engineering, product managers, sales and finance) to understand data requirements and deliver data solutions.

• Enhance data infrastructure for performance, efficiency to meet evolving business needs.

Minimum qualifications

• Bachelor's degree in Computer Science, Mathematics, a related field, or equivalent practical experience.

• 3 year of experience with data processing software (e.g., Hadoop, Spark, Pig, Hive) and algorithms (e.g., Map Reduce, Flume).

• Experience with database administration techniques or data engineering, and writing software in Java, C++, Python, Go, or JavaScript.

• Experience managing client-facing projects, troubleshooting technical issues, and working with Engineering and Sales Services teams.

Preferred qualifications

• Experience working with data warehouses, including data warehouse technical architectures, infrastructure components, ETL/ELT, and reporting/analytic tools and environments.

• Experience working with Big Data, information retrieval, data mining, or machine learning.

• Experience in building applications with modern web technologies (e.g., NoSQL, MongoDB, SparkML, Tensor Flow).

• Experience architecting, developing software, or production-grade Big Data solutions in virtualized environments.