Minimum qualifications:
- Bachelor’s degree in Computer Science, Data Engineering, Data Science, Management Information Systems, or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages (e.g., C/C++, Java, Python, R) and database languages (e.g., SQL).
- 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
- Experience in developing and scaling data pipelines for data ETL (Extraction, Transformation, Load), machine learning models, or other data products.
- Experience with Business Intelligence tools (e.g., Tableau, Looker), AI agents, or cloud platforms..
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- Experience in quantitative analytics including data engineering, data analysis, machine learning, or model deployment.
- Experience managing sensitive user data, including company compliance (e.g., product log policies).
- Ability to balance tests, business logic, data quality, and infrastructure problems within an agile team.
- Ability to develop, manage, and maintain data science infrastructure and tools, ensuring reliability and performance.
- Ability to design automated solutions to process large quantities of unstructured data (product logs, panel data, large text corpuses, etc.).
About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.At Google, data drives decision-making and engineering is what makes accessing and utilizing that data possible. This team works at the intersection of engineering and data science.
You will focus on the engineering tests in managing large and unstructured datasets. Your responsibilities will span designing and implementing systems for large-scale data processing, architecting schema designs and data models, and ensure the integrity and privacy of our data.
You will build the infrastructure that enables insights. You will collaborate with Data Scientists, Product Managers, and other teams to understand the needs and deliver solutions that allow Google to analyze data. Your work ensures that insights can be generated and operationalized at Google's scale, impacting everything from product development to operational efficiency.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
- Collaborate with data scientists, researchers, and engineers to design, prototype, productionize, deploy, and monitor data products (e.g., metrics, dashboards, pipelines, models, reports, etc.) and help technical and non-technical partners understand and interpret data products.
- Implement data anonymization policies that comply with user privacy requirements.
- Explore new data sources and develop solutions for data integration, sanitization, and storage.
- Manage and optimize the team's data infrastructure (tooling, monitoring, security, reliability, performance) and proactively identify and resolve performance bottlenecks and data quality issues.
- Develop and deploy machine learning models to solve business problems, leveraging programming skills and an understanding of statistical and machine learning techniques.