Data Engineer, Play Data Science and Analytics

Data Engineer, Play Data Science and Analytics

About the job

Google Play provides apps, games, and digital content services that bring Android devices to life. The Play Store serves over four billion users around the world, and is a critical driver of Google’s overall business growth. The Play Data Science and Analytics (DSA) team works on a variety of challenging data science projects to drive product and go-to-market decisions for Play. Our goal is to Power Play’s growth by building a deep understanding of our users and developers, enabling data-driven decision making, through insights, thought leadership, and unified data foundations.As a Data Engineer on the Play Data Science and Analytics team, you will take a significant role in designing and building the next generation of our data infrastructure. You will be responsible for architecting, implementing, and optimizing complex, scalable data pipelines, moving beyond basic development to own key components of our data warehouse. This role requires a technical expert who can manage massive datasets, write highly efficient SQL and Python code, and collaborate effectively with senior stakeholders and engineers. You will build innovative data foundations and AI-driven insights solutions while helping to define the standards and best practices that elevate the entire team, driving data quality and AI-readiness initiatives.

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 3 years of experience in a data engineering, data infrastructure, or data analytics role.
• Experience with database administration techniques or data engineering, as well as writing software in Java, C++, Python, Go, or JavaScript.

Preferred qualifications:

• Experience with data warehouses, including data warehouse technical architectures, infrastructure components, ETL/ELT, and reporting/analytic tools and environments.
• Experience with data analysis, including statistics, and ML model development (data preparation, model selection, evaluation, tuning).
• Experience in scripting languages like Python for data manipulation, analysis, and automation.
• Ability to monitor, troubleshoot, and tune data systems and pipelines to improve efficiency.
• Ability to develop tools and systems to automate data processes, and increase overall efficiency, with proficiency in programming languages (e.g., SQL, Python), producing readable and well-structured code.
• Ability to deliver and maintain data projects from conception to production.

Responsibilities

• Design, build, and maintain scalable data pipelines to ingest, process, and store data from various sources. Implement data quality checks and monitoring to ensure accuracy and integrity.
• Write complex SQL queries for data extraction and transformation to enable ad-hoc analysis and automated reporting. Conduct quantitative analysis to support business decisions.
• Develop and manage scalable data foundations and models specifically designed to support AI/ML initiatives and AI-driven insights.
• Develop, test, and deploy intelligent agents using Python and the Google ADK framework to automate tasks like data analysis and system orchestration.
• Partner with executive stakeholders and data scientists.