Data Engineer

Urban SDK develops geospatial AI technology for public-sector decision-making. The Data Engineer designs, builds, and maintains scalable data pipelines and platforms for geospatial traffic analytics, including productionizing machine learning models and ensuring reliable data availability.

Responsibilities:

• Design, implement, and maintain scalable data pipelines and ETL/ELT workflows on Databricks and cloud platforms
• Manage large-scale geospatial and temporal datasets stored in AWS S3
• Collaborate with data scientists to productionize machine learning models and ensure smooth data availability
• Implement data validation, testing, and monitoring frameworks to ensure data accuracy, consistency, and reliability
• Optimize data storage and processing strategies to handle high volumes of traffic and mobility data efficiently
• Develop and maintain documentation for data workflows, architecture, and processes
• Work closely with cross-functional teams to understand data requirements and ensure timely delivery
• Stay up-to-date with the latest trends and best practices in data engineering, cloud technologies, and big data processing

Requirements:

• Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field
• 3+ years of experience as a data engineer or in a similar role
• Strong proficiency in Python and associated libraries for data engineering (pandas, PySpark, etc.)
• Hands-on experience with Databricks and Spark for large-scale data processing
• Experience with AWS services, especially S3, and knowledge of cloud-based data architectures
• Solid understanding of data pipeline testing, version control, and CI/CD practices
• Experience with SQL and NoSQL databases
• Strong problem-solving skills and attention to detail
• Familiarity with geospatial data formats and processing (GeoJSON, Shapefiles, PostGIS)
• Experience with workflow orchestration tools (Databricks, Prefect, or similar)
• Knowledge of containerization (Docker/Kubernetes) and cloud-native data solutions
• Experience supporting machine learning pipelines in production