data engineer

Services Finance Data Science and Engineering team is looking for a passionate and highly motivated Data Engineer to drive our financial data platform forward. You will provide a key function in shaping the success of Apple's current and future products. As members of the Services Finance Data Science and Engineering team, we work with various business and engineering teams to understand current and future business initiatives. We need to be persistent and flexible in extracting data from various sources, cleaning and curating this data, and then clearly and concisely communicating insights.\n

The individual in this role will collaborate with data scientists, business analysts and subject matter experts (SME) to acquire, and transform raw data and develop sophisticated data products. This includes developing and maintaining data pipelines, extracting from different sources (databases, APIs, etc.), and transforming raw data into normalized tables after running data quality checks. A successful data engineer is skilled in taking a business problem and translating it into a data solution in close collaboration with data scientists, business analysts, and SMEs.

Minimum of 5 years of working experience as a data engineer building end-to-end data pipelines\nAdvanced SQL skills for complex transformations, optimization, and troubleshooting\nA Bachelor's degree in Statistics, Computer Science, Computer Engineering, or equivalent practical experience\nPython proficiency, comfortable writing clean, maintainable Python for data pipelines and automation\nHands-on experience working with relational database management system such as Snowflake, PostgreSQL or similar cloud data warehouses\nAbility to work both independently and within a team environment\nStrong written and verbal communication skills, capable of explaining technical results to a non-technical audience

Experience with scheduling systems and CI/CD tools such as Airflow, Jenkins, etc.\nUnderstanding of dimensional modeling, data warehousing concepts, medallion and data lakehouse architectures\nHands-on experience with building ETL process and writing unit tests using DBT or similar tools\n