Senior Data Engineer

Ryan Companies US, Inc.•Minneapolis, MN

3d•$115,000 - $144,000•Hybrid

About The Position

Ryan Companies has an immediate need for a Senior Data Engineer. This is a 4-days-per-week IN-OFFICE opportunity for our Minneapolis headquarters, providing 1 day work-from-home each week. The Senior Data Engineer will play a foundational role in building and maintaining the data infrastructure that enables advanced analytics, machine learning, and decision intelligence across Ryan Companies. This role will focus on developing scalable, reliable data pipelines that ensure the quality, accessibility, and performance of data used across all of Ryan Companies. The successful candidate will work cross-functionally with data architects, AI/ML engineers, data scientists, analysts, and domain experts to design and implement modern data engineering solutions. With more than 88 years in business and 16 offices (and growing!), Ryan offers comprehensive commercial real estate solutions to customers across the US. We bring a collaborative, relationship-driven approach to delivering spaces where people thrive and businesses succeed. Join a company with an outstanding culture and competitive benefits. At Ryan, we don’t just build buildings – we build stories.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related technical field.
5+ years of professional experience in data engineering or a similar role.
Strong programming skills in Python, SQL, with experience using tools such as Cloud Composer.
Proven experience designing and maintaining data pipelines using platforms such as GCP, AWS, Azure or similar.

Nice To Haves

Experience building data infrastructure and services in cloud-native environments (GCP preferred, with exposure to AWS).
Familiarity with MLOps or experience supporting machine learning pipelines in production environments.
Exposure to domain-driven architecture.
Strong collaboration and communication skills with a track record of partnering across functions to deliver high-impact solutions.

Responsibilities

Architect, build, and maintain scalable data pipelines on Google Cloud Platform (GCP), utilizing services like BigQuery, Dataflow, and Cloud Storage to handle diverse data sources (e.g., project systems, safety logs, IoT/sensor data).
Design and implement effective data models, including star schemas and dimensional modeling, to support business intelligence and analytics.
Develop and manage data warehousing solutions to ensure efficient data storage, retrieval, and cost-effectiveness.
Contribute to metadata management, data cataloging, and lineage tracking to enhance the discovery and transparency of enterprise data assets.
Design and orchestrate robust ETL/ELT processes and data integration workflows using tools like Cloud Composer or Apache Airflow.
Implement and manage both batch and real-time data streaming pipelines to ensure timely and accurate data availability for downstream applications.
Write clean, efficient, and well-documented Python and SQL code to process and transform large, complex datasets.
Monitor, troubleshoot, and optimize data pipeline performance, identifying and resolving bottlenecks to improve efficiency and scalability.
Implement and maintain data quality frameworks to ensure the accuracy, consistency, and reliability of data across all systems.
Develop and implement data validation and testing procedures to maintain the highest standards of data integrity.
Establish and enforce data governance policies and best practices, ensuring all data handling is secure, private, and compliant with regulations.
Build and maintain CI/CD pipelines for the automated testing and deployment of data engineering workflows.
Support the deployment and monitoring of machine learning models by implementing reproducible and traceable data environments.
Partner with DevOps and Technology teams to automate infrastructure provisioning, CI/CD processes, and data quality monitoring.
Partner with Enterprise Architects, Solution Architects, data scientists, and analysts to understand data needs, design technical solutions, and translate business requirements into architectural designs.
Lead the implementation of data solutions, from discovery and design through to deployment.
Provide mentorship and guidance to other data engineers, fostering a culture of knowledge sharing and continuous improvement.
Create and maintain clear documentation for architecture, schemas, and pipeline workflows to support team knowledge and onboarding.