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Revenue Platforms Data Engineer

🌎

Sydney NSW, Australia

9h ago
πŸ‘€ 1 views
πŸ“₯ 0 clicked apply

Job Description

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 in 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 deliver operational process improvements.
  • Excellent communication and customer service skills.
  • Passion for developing and analyzing data sets and converting them into business insights.

In this role your mission is to simplify and centralize the prioritization and deployment of smart, Quote-to-Report (Q2R) digitalization to deliver the 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.

  • Collaborate with Stakeholders, work with finance Subject matter experts to understand report requirements and provide necessary data solutions, access and support.
  • Develop and Maintain Data Pipelines design and implement Extract, Transform, and Load (ETL) processes (SQL, Python) to ensure timely collection and integration of financial data from various sources.
  • Manage Data Architecture understands internal infrastructure to select appropriate sources and technologies to manage data, and design data architecture to support financial reporting. Ensure data security and compliance.
  • Ensure Data Quality implements data validation and cleansing processes to maintain quality data for accurate business reporting.
  • Automate manual tasks and design data solutions. Efficiently manage volumes of financial data.

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