Evans Transportation is seeking a Data Engineer to design, build, and maintain the data infrastructure that powers analytics, reporting, and data-driven decision-making. You will own the pipelines that move and transform data from source systems into clean, reliable, well-modeled datasets used by analysts, data scientists, and business teams. Responsibilities include designing, building, and maintaining scalable batch and streaming data pipelines (ETL/ELT); modeling data in data warehouses and lakehouses to support reporting and analytics use cases; integrating data from databases, APIs, third-party platforms, and event streams; ensuring data quality, reliability, and observability through testing, monitoring, and alerting; optimizing pipeline and query performance and managing cost and scalability; implementing data governance, security, and access-control best practices; collaborating with analysts, data scientists, and software engineers to deliver usable data products; and documenting data flows, schemas, and processes to maintain the platform. Qualifications include 3+ years in data engineering (strong SQL and programming skills, preferably .NET and Python), solid data modeling and warehousing knowledge, experience with cloud data warehouses or lakehouses, and experience building pipelines with orchestration tools (e.g., Airflow, Dagster, dbt). Familiarity with a major cloud provider (preferably Azure) is expected. Nice-to-have capabilities include exposure to data governance, lineage or catalog tools; infrastructure-as-code and CI/CD experience (Terraform, Docker, Git

Evans Transportation is seeking a Data Engineer to design, build, and maintain the data infrastructure that powers analytics, reporting, and data-driven decision-making. You will own the pipelines that move and transform data from source systems into clean, reliable, well-modeled datasets used by analysts, data scientists, and business teams. Responsibilities include designing, building, and maintaining scalable batch and streaming data pipelines (ETL/ELT); modeling data in data warehouses and lakehouses to support reporting and analytics use cases; integrating data from databases, APIs, third-party platforms, and event streams; ensuring data quality, reliability, and observability through testing, monitoring, and alerting; optimizing pipeline and query performance and managing cost and scalability; implementing data governance, security, and access-control best practices; collaborating with analysts, data scientists, and software engineers to deliver usable data products; and documenting data flows, schemas, and processes to maintain the platform. Qualifications include 3+ years in data engineering (strong SQL and programming skills, preferably .NET and Python), solid data modeling and warehousing knowledge, experience with cloud data warehouses or lakehouses, and experience building pipelines with orchestration tools (e.g., Airflow, Dagster, dbt). Familiarity with a major cloud provider (preferably Azure) is expected. Nice-to-have capabilities include exposure to data governance, lineage or catalog tools; infrastructure-as-code and CI/CD experience (Terraform, Docker, Git