Seeking a seasoned Data Engineer with extensive experience in DBT, Apache Airflow, and Python. This role focuses on architecting, building, and maintaining robust data pipelines and transformation frameworks. You will be instrumental in constructing modern data platforms, orchestrating complex workflows, and developing ETL/ELT processes, leveraging cloud-based data warehousing solutions.

This is a full-time opportunity for a professional ready to contribute to our data infrastructure.

Key responsibilities include designing and developing scalable ETL/ELT data pipelines using Python, DBT, and Apache Airflow. You will build and manage Airflow DAGs for workflow orchestration and develop reusable, optimized DBT models. Implementing data quality checks, validation frameworks, and reconciliation processes is crucial. You will also write and optimize Python scripts for data ingestion and automation, work with various data formats, and integrate data from diverse sources.

Troubleshooting pipeline issues, collaborating with stakeholders, and ensuring adherence to best practices are also core duties. Production support and incident resolution will be part of your remit.

A minimum of 5 years of dedicated experience in Data Engineering is essential. Candidates must possess strong, hands-on expertise with DBT and extensive experience in Apache Airflow, including DAG development and workflow orchestration. Proficiency in Python for ETL development and automation, coupled with strong SQL skills and experience in performance tuning, is required.

Experience developing and supporting enterprise-scale ETL/ELT pipelines, knowledge of data modeling concepts like Star Schema, and familiarity with Git-based version control systems are mandatory. A Bachelor's degree in a relevant field like Computer Science or Engineering is expected.