Engineer III, Global Technology - Data Solutions

Expeditors•Edison, GA

19h•Onsite

About The Position

As an Engineer III (Data Engineer) on Expeditors’ Data Solutions team, you will design, build, and support enterprise-scale data and analytics solutions that enable trusted, data-driven business decisions. You will contribute to modern analytics capabilities and engineering excellence, ensuring data products are scalable, reliable, maintainable, and aligned with business objectives. You will work with engineers, product managers, architects, platform teams, and business stakeholders to develop enterprise data platforms, analytical applications, semantic models, and reporting solutions. The technology environment includes Microsoft Fabric, Databricks, Power BI, SQL, Spark, PySpark, Git-integrated CI/CD pipelines, and modern data engineering practices. The position is on-site in one of the following locations, depending on availability: Atlanta, Georgia; Dallas, Texas; Detroit, Michigan; Laredo, Texas; or Newark, New Jersey. Candidates must be local or willing to relocate. Relocation assistance is not offered. Applicants must be eligible to work lawfully in the United States immediately; the employer will not transfer or sponsor U.S. work authorization for this opportunity.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Business, or a related field, or an equivalent combination of education and experience.
At least 5 years of experience designing and implementing end-to-end enterprise data and analytics solutions.
Advanced experience with Microsoft Fabric and Databricks, including data engineering, data modeling, semantic-layer development, analytics architecture, and performance optimization.
Advanced proficiency with Power BI, including DAX, Power Query, semantic models, dashboards, and visualization best practices.
Experience building and supporting scalable data pipelines, Lakehouse solutions, data warehouses, and transformation processes using SQL, Spark, or PySpark.
Experience implementing Git-integrated CI/CD practices, automated testing, deployment pipelines, and release governance.
Strong communication, collaboration, problem-solving, and technical leadership skills.
Eligibility to lawfully work in the United States immediately; the employer will not transfer or sponsor applicants for U.S. work authorization.

Nice To Haves

Experience with Agentic AI development processes and practices.
Experience defining and governing enterprise KPIs, business metrics, semantic models, and reporting standards.
Experience implementing data quality, observability, privacy, security, and governance practices across analytics platforms.
Experience modernizing legacy reporting and analytics solutions.
Experience with Tableau, Python visualization libraries, Azure Data Services, or similar analytics technologies.
Experience measuring and improving engineering delivery through DORA metrics.
Microsoft Fabric, Power BI, Databricks, Azure, or related data and analytics certifications.
One or more of the following certifications: Microsoft Certified: Fabric Analytics Engineer Associate (DP-600), Microsoft Certified: Fabric Data Engineer Associate (DP-700), Microsoft Certified: Power BI Data Analyst Associate (PL-300), or Databricks Data Engineer Professional.

Responsibilities

Design, develop, and support enterprise data models, semantic layers, reporting datasets, and analytical applications.
Establish engineering best practices for data quality, testing, automation, observability, CI/CD, and release governance.
Design scalable analytics architectures that meet current business needs and support future growth.
Participate in solution design and architecture discussions to improve reliability, scalability, and maintainability.
Partner with product managers, architects, platform engineers, and business stakeholders to deliver analytical solutions.
Support team success through code reviews, technical collaboration, mentoring, and knowledge sharing.
Define and standardize KPIs, business metrics, and semantic models for trusted enterprise reporting.
Promote engineering best practices and advocate for solutions that improve delivery quality and customer outcomes.
Translate complex business requirements into scalable analytical solutions with a focus on long-term supportability.
Identify opportunities to modernize analytics capabilities and evolve enterprise reporting platforms.
Deliver reporting, dashboards, and visualizations that improve operational visibility and decision-making.
Ensure analytics solutions align with enterprise data governance, privacy, security, and compliance requirements.
Support production analytics solutions through monitoring, troubleshooting, and performance optimization.