Join our expanded team as a Data Migration Engineer and play a crucial role in transforming complex data workflows. This position focuses on leveraging modern data engineering tools and practices to replicate and enhance existing Informatica processes.

You will be instrumental in building robust data pipelines and transformation models, ensuring seamless and efficient data migration to our target data warehouse. This is an excellent opportunity to contribute to significant data initiatives and refine your skills in a dynamic environment.

Your primary responsibilities will involve assessing Informatica workflows, including sessions, mappings, and parameterizations, and translating them into equivalent Python EL scripts and dbt transformation models.

You will develop and maintain Python EL pipelines for high-volume data landing, design dbt transformation models adhering to best practices, and implement comprehensive testing strategies using dbt-native tests and custom Python unit tests.

Orchestrating these processes end-to-end using Airflow DAGs will be key, along with contributing to GitLab CI/CD pipelines for code quality and deployment. You will also perform peer code reviews, troubleshoot performance issues, and contribute to technical documentation.

We are seeking individuals proficient in translating complex Informatica workflows into Python EL scripts and dbt transformation models.

Essential skills include developing Python EL pipelines using SQLAlchemy, cx_Oracle, pyodbc, and bulk export tools like Oracle Data Pump and SQL Server BCP. Expertise in designing dbt transformation models with an understanding of model layering, macros, incremental strategies, and snapshot patterns is required.

Experience with Airflow for orchestrating Python EL and dbt scripts, familiarity with GitLab CI/CD for dbt and Airflow code, and the ability to troubleshoot performance and data discrepancies are also necessary. A strong understanding of data quality and validation techniques is important.