Services Finance Data Engineer

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. Building and maintaining pipelines to extract data from various external sources (e.g. databases, APIs, etc..) and move data to internal destinations (e.g. Snowflake, Cloud Storage, etc..) Maintain data dictionary, lineage and documentation Enhance existing Pipeline monitoring and develop functionality for data observability Enable Analysts and Tableau developers by curating datasets which are easily accessible Work with SME to understand the data requirements and to translate business problems into data solutions and products Monitor the health status of the tables and data pipelines and develop an alerting system for failures and problems Conduct POCs and test new methods and technologies based on the team needs Minimum of 5 years of working experience as a data engineer building end-to-end data pipelines Advanced SQL skills for complex transformations, optimization, and troubleshooting A Bachelor’s degree in Statistics, Computer Science, Computer Engineering, or equivalent practical experience Python proficiency, comfortable writing clean, maintainable Python for data pipelines and automation Hands-on experience working with relational database management system such as Snowflake, PostgreSQL or similar cloud data warehouses Ability to work both independently and within a team environment Strong 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. Understanding of dimensional modeling, data warehousing concepts, medallion and data lakehouse architectures Hands-on experience with building ETL process and writing unit tests using DBT or similar tools

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