Data Engineer, Enterprise Platforms at Google

About the job

As a Data Engineer for the Enterprise Platforms team, you will play a vital role in building and maintaining the data infrastructure that fuels our product strategy. You will design, develop, and optimize data pipelines, ensuring data quality and accessibility for advanced analytics. Your technical expertise will enable the product team to leverage data-driven insights to optimize product feature adoption and performance and measure the impact of strategic initiatives.

To accelerate the growth and market leadership of Enterprise Buying Platforms (DV360 and SA360), you will answer critical business questions and deliver actionable, data-driven insights that inform product and commercial strategy. The Enterprise Platform Data Science team provides quantitative support, market understanding and a strategic perspective to our partners throughout the organization, in close collaboration with the Ads & Commerce Finance team.
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 best practice 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 scalable and reliable data pipelines to collect, process, and store data from various data sources.

• Implement robust 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 impactful data solutions.

• Optimize data infrastructure for performance, efficiency, and scalability to meet evolving business needs.

Minimum qualifications

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

• 1 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, as well as 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 multi-tier high availability applications with modern web technologies (e.g., NoSQL, MongoDB, SparkML, Tensor Flow).

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