Data Engineer

Saxon Global•Madison, WI

About The Position

We are seeking a highly skilled and experienced Data Engineer to join our team. The ideal candidate will have a strong background in data engineering principles, expert-level proficiency in Python, and extensive hands-on experience with Google Cloud Platform (GCP) services. This role requires a deep understanding of building and optimizing enterprise-scale ETL/ELT pipelines, working with large datasets, and implementing CI/CD and DevOps practices. The successful candidate will be able to lead technical initiatives and effectively manage stakeholders.

Requirements

  • Bachelor's Degree in Computer Science, Engineering, Information Systems, or related field.
  • 8-10+ years of Data Engineering experience.
  • 3+ years in a Lead, Principal, or Senior Data Engineering capacity.
  • Expert-level proficiency with Python for data engineering and data processing.
  • Strong hands-on experience with Google Cloud Platform (GCP).
  • Extensive experience with BigQuery.
  • Extensive experience with Dataflow.
  • Extensive experience with Dataproc / PySpark.
  • Extensive experience with Cloud Composer (Airflow).
  • Extensive experience with Pub/Sub.
  • Extensive experience with Cloud Storage.
  • Strong SQL development and data modeling experience.
  • Experience building enterprise-scale ETL/ELT pipelines.
  • Experience working with large-volume datasets and performance optimization.
  • Knowledge of CI/CD, Git, DevOps practices, and automated deployment frameworks.
  • Strong communication and stakeholder management skills.
  • Ability to work independently and lead technical initiatives from concept through production.

Responsibilities

  • Design, build, and maintain scalable ETL/ELT pipelines.
  • Develop and optimize data processing jobs using Python and BigQuery.
  • Utilize GCP services such as Dataflow, Dataproc/PySpark, Cloud Composer (Airflow), Pub/Sub, and Cloud Storage.
  • Perform strong SQL development and data modeling.
  • Work with large-volume datasets and implement performance optimizations.
  • Implement CI/CD, Git, and DevOps practices for automated deployment.
  • Lead technical initiatives from concept to production.
  • Communicate effectively with stakeholders and manage relationships.
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