GCP Data Engineer

Location: Hybrid - 4 days/week in SE Michigan office
Duration: 12+ months

About the role

The Data Engineer is responsible for designing, building, and maintaining scalable data solutions that support enterprise analytics, machine learning, and business intelligence initiatives. This role develops and optimizes data infrastructure, pipelines, and platforms that enable the efficient collection, storage, processing, and analysis of large volumes of structured and unstructured data. The ideal candidate will possess strong expertise in cloud-based data engineering, data modeling, pipeline development, and modern data platforms, while partnering closely with business and technology stakeholders to deliver reliable, high-performing data solutions.

Responsibilities

• Collaborate with business and technology stakeholders to understand current and future data requirements and translate them into scalable technical solutions.
• Design, develop, and maintain reliable, efficient, and scalable data infrastructure supporting data collection, storage, transformation, and analytics.
• Build and optimize end-to-end data pipelines, workflows, and data models to ensure accurate and efficient data processing.
• Design, implement, and support enterprise data platforms, including data warehouses, data lakes, and lakehouse architectures.
• Develop tools, frameworks, scripts, and automation capabilities that improve data engineering efficiency and reduce manual effort.
• Create and maintain robust data integration solutions across multiple systems and sources.
• Ensure data quality, performance, reliability, and scalability through monitoring, testing, and continuous optimization.
• Partner with data scientists, analysts, and application teams to support advanced analytics and machine learning initiatives.
• Identify opportunities to improve data architecture, pipeline performance, cost optimization, and operational efficiency.
• Implement data governance, security, and best practices across the data ecosystem.

Requirements

• 5+ years of professional experience in Data Engineering.
• Hands-on experience designing, developing, and maintaining modern data architectures and data platforms.
• Strong experience with Google Cloud Platform (GCP) services and cloud-native data solutions.
• Advanced knowledge of BigQuery for data warehousing, analytics, and large-scale data processing.
• Strong proficiency in Python for data engineering, automation, and data pipeline development.
• Experience developing reusable frameworks, scripts, and automation solutions.
• Proficiency using GitHub and modern source control practices.
• Experience designing and implementing data warehouses, data lakes, and lakehouse architectures.
• Strong understanding of data modeling concepts and relational database design.
• Experience building scalable ETL/ELT pipelines and data integration solutions.
• Experience supporting machine learning and advanced analytics workloads through scalable data solutions.
• Ability to work with large, complex datasets to support business intelligence and predictive analytics initiatives.

Preferred Qualifications

• Experience with machine learning frameworks such as TensorFlow and Scikit-learn.
• Understanding of algorithms and statistical modeling techniques.
• Experience supporting data science and AI/ML initiatives.
• Exposure to real-time or streaming data architectures.
• Experience implementing enterprise-scale data governance and data quality solutions.

Skills

• Google Cloud Platform (GCP)
• BigQuery
• Python
• GitHub
• Data Engineering
• ETL/ELT Development
• Data Pipelines
• Data Warehousing
• Data Lakes
• Data Modeling
• Machine Learning Enablement
• TensorFlow (preferred)
• Scikit-learn (preferred)
• Algorithms (preferred)
• Relational Databases (preferred)
• Advanced Data Modeling (preferred)
• Data Lakehouse Architecture (preferred)

Qualifications

• Master's Degree in Computer Science, Data Science, Engineering, Information Systems, or a related technical field (required).
• Advanced certifications in Cloud, Data Engineering, Analytics, or Machine Learning (preferred).

Benefits

• Supplemental medical, dental, vision, life, and disability insurance plans.
• Employee Stock Purchase Program (ESPP).
• 401K program with company match after 12 months of tenure.
• Health Savings Account (HSA) on the HDHP plan.
• SupportLinc Employee Assistance Program (EAP) with up to 8 free counseling sessions.
• Corporate discount savings program and other discounts.
• On-demand training program.
• Access to certification prep, technical and leadership courses/books/seminars after 6+ months of tenure.
• Certification discounts and perks for associations including CompTIA and IIBA.
• Dedicated customer service team for consultants.
• Access to a certified Career Coach.