Revenue Platforms Data Engineer

In this role, your mission is to simplify and centralize the prioritization and deployment of smart, Quote-to-Report (Q2R) digitalization to deliver the highest Return on Investment (ROI) on Finance platforms, for Alphabet. We aim to enable Q2R systems, processes and data fit for Finance that allow every product, anywhere, anytime, the right way, effortlessly.

Responsibilities include:

1. Collaborating with Stakeholders: Work with finance Subject Matter Experts to understand report requirements and provide necessary data solutions, access and support.
2. Developing and Maintaining Data Pipelines: Design and implement ETL processes (SQL, Python) to ensure timely and accurate collection and integration of financial data from various sources.
3. Managing Data Architecture: Understand internal infrastructure to select appropriate sources and technologies to manage data, and design data architecture to support financial reporting. Ensure data security and compliance.
4. Ensuring Data Quality: Implement data validation and cleansing processes to maintain quality data for accurate revenue reporting.
5. Automating manual tasks and designing scalable data solutions to efficiently handle volumes of financial data.

Google is committed to building a diverse workforce and creating a culture of belonging. They provide equal employment opportunities regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other protected basis.

Minimum Qualifications:

• Bachelor's degree in Business, Finance, a related quantitative field, or equivalent practical experience.
• 3 years of experience with Finance systems, accounting, operations, tax, internal controls, requirements documentation, testing, and validation.
• Experience with SQL/database management.
• Experience with Object-Oriented Programming.

Preferred Qualifications:

• Experience in collaborative coding and version control.
• Experience in machine learning, including data preparation, model selection, performance evaluation, and parameter tuning.
• Ability to maintain professional and influential presence with excellent communication and customer service skills.
• Ability to drive operational process improvements.
• Passion for developing and analyzing complex data sets and converting them into actionable business insights.