Join our team as a Data & Analytics Principal Consultant and play a pivotal role in modernizing our data pipelines. This position involves translating complex existing workflows into efficient, scalable Python and dbt solutions, ensuring high data quality and seamless integration.

You will leverage your expertise in data transformation and pipeline orchestration to build robust systems capable of handling large volumes of data, driving critical business insights.

Key responsibilities include assessing Informatica workflows and rewriting them as Python EL scripts and dbt transformation models.

Develop and maintain Python EL pipelines for high-volume data ingestion into data warehouses using tools like SQLAlchemy and Oracle Data Pump. Design and implement dbt transformation models, adhering to best practices for layering, macros, and incremental strategies.

Create dbt-native and custom Python unit tests to ensure transformation accuracy and data integrity. Orchestrate end-to-end data pipelines using Airflow DAGs, ensuring functional parity with source Informatica workflows.

Contribute to GitLab CI/CD pipelines for code quality and automated deployment. Conduct peer code reviews and provide valuable technical feedback. Troubleshoot performance issues and data discrepancies during testing phases, including reconciliation between source and target systems.

Assist in creating comprehensive technical documentation, runbooks, and handover materials.

This role requires a strong background in data engineering and analytics, with a proven ability to work with complex data systems.

Experience in translating Informatica workflows into Python EL scripts and dbt transformation models is essential. Proficiency in Python, SQLAlchemy, cx_Oracle, and pyodbc is necessary for developing data pipelines.

Familiarity with bulk export tools like Oracle Data Pump and SQL Server BCP is required. Expertise in dbt, including model layering, macros, incremental strategies, and snapshot patterns, is critical for developing transformation models.

Experience with Airflow for orchestrating data pipelines and GitLab CI/CD for deployment and testing is expected. The ability to perform peer code reviews and troubleshoot performance issues is also key. Strong documentation skills are a plus.