Senior Data Engineer - 4 Day/Week In-Office + 1 WFH

Ryan Companies US, Inc.; Minneapolis (MN); Hybrid; USD 115,000 - 144,000; Full time

Benefits offered by this job

Competitive Salary

Comprehensive Benefits

Job summary

Ryan Companies US, Inc. is seeking a Senior Data Engineer for a four-day-per-week in-office role at our Minneapolis headquarters, with one day of remote work each week.

You will build and maintain data infrastructure enabling analytics, ML, and decision intelligence, focusing on scalable data pipelines. You will collaborate with data architects, AI/ML engineers, data scientists, and analysts to design modern data solutions on GCP, using BigQuery, Dataflow, and Cloud Storage, while ensuring data

Qualifications

• Bachelor's or Master's degree in a related technical field.
• 5+ years of professional data engineering experience.
• Strong Python and SQL coding skills; experience with Cloud Composer.
• Proven experience designing and maintaining data pipelines on cloud platforms (GCP/AWS/Azure).

Responsibilities

• Architect, build, and maintain scalable data pipelines on GCP using BigQuery, Dataflow, and Cloud Storage.
• Design data models, star schemas, and warehousing for BI/analytics.
• Develop ETL/ELT processes and real-time data streams; write clean Python/SQL code.
• Ensure data quality, governance, and metadata management across systems.

Skills

Python

SQL

ETL/ELT

Data pipelines

GCP

BigQuery

Dataflow

Airflow

Cloud Storage

Education

Bachelor's or Master's in Computer Science/Engineering/Information Systems

Tools

Cloud Composer

Apache Airflow

Job description

Ryan Companies US, Inc. is seeking a Senior Data Engineer for a four-day-per-week in-office role at our Minneapolis headquarters, with one day of remote work each week.

You will build and maintain data infrastructure enabling analytics, ML, and decision intelligence, focusing on scalable data pipelines. You will collaborate with data architects, AI/ML engineers, data scientists, and analysts to design modern data solutions on GCP, using BigQuery, Dataflow, and Cloud Storage, while ensuring data