We are looking for a Senior Data Engineer to strengthen our data foundation and help deliver reliable, scalable information solutions across Minneapolis, Minnesota. In this role, you will create and optimize modern data platforms that power analytics, reporting, and machine learning initiatives. You will collaborate with technical and business partners to turn complex source data into trusted, accessible assets that support better decisions across the organization.
Responsibilities:
- Build and maintain scalable data pipelines that ingest, transform, and deliver information from a variety of enterprise and operational data sources.
- Design data models and warehouse structures that improve reporting performance, support analytics use cases, and enable long-term scalability.
- Develop and orchestrate ETL and ELT workflows using cloud-based and workflow automation tools to ensure dependable data movement across systems.
- Create batch and near-real-time processing solutions that provide timely, accurate datasets for downstream applications and analytical teams.
- Write efficient, well-structured Python and SQL code to cleanse, transform, and validate large and complex datasets.
- Monitor pipeline health, investigate failures, and tune jobs for reliability, cost control, and overall platform performance.
- Establish data quality checks, testing routines, and governance practices to maintain consistency, integrity, and compliance across the data environment.
- Partner with architects, analysts, data scientists, AI/ML engineers, and DevOps teams to support production-ready data products, automated deployments, and reproducible machine learning workflows.
- 5+ years of experience in data engineering, data integration, or a closely related field.
- Experience in architecture, engineering, or construction industry is big plus
- Strong hands-on expertise with Python for data processing, automation, and pipeline development.
- Advanced SQL skills, including experience designing queries and transformations for large-scale datasets.
- Experience building ETL or ELT workflows and maintaining reliable data pipelines in production environments.
- Working knowledge of Apache Spark for distributed data processing and large-volume workloads.
- Experience with Microsoft SQL Server and data warehousing concepts such as dimensional modeling or star schema design.
- Familiarity with machine learning pipelines and the data engineering practices needed to support model deployment and monitoring.
