Data Engineer

Role overview Design and operate scalable data pipelines and platforms that support geospatial and traffic analytics. The role focuses on processing large spatial and time-based datasets in cloud environments, maintaining dependable data quality, and making data available for analytical and machine learning work.

Responsibilities - Build and maintain ETL/ELT pipelines and workflows using Databricks, Spark, and cloud services. - Manage large geospatial and temporal datasets stored in AWS S3. - Work with data scientists to productionize machine learning models and provide reliable data inputs. - Develop validation, automated testing, and monitoring practices to track data quality and pipeline reliability. - Tune storage and processing approaches for high volumes of mobility data. - Document data workflows, architecture, and operating processes, and coordinate delivery across teams.

Requirements - A bachelor’s or master’s degree in computer science, data engineering, or a related discipline. - At least three years of data engineering or comparable experience. - Strong Python skills and experience with libraries such as pandas or PySpark. - Practical experience with Databricks and Spark for large-scale processing. - AWS experience, especially S3, and understanding of cloud data architectures. - Familiarity with SQL and NoSQL databases, version control, pipeline testing, and CI/CD. - Strong problem-solving skills and attention to detail.

Nice to have - Experience with geospatial formats or tools such as GeoJSON, Shapefiles, or PostGIS. - Familiarity with orchestration tools such as Prefect, containerization, or cloud-native data services. - Experience supporting machine learning pipelines in production.

Benefits and work setup Full-time position, based in Jacksonville, Florida, or remote. The stated package includes an experience-based salary, annual bonus, medical, dental and vision coverage, a 401(k), 21 vacation days, and lunch at the office.