Data Engineer

Summary

Urban SDK is a geospatial AI technology company focused on providing public leaders with insights and automation for mission-critical smart-city decisions. The Data Engineer will design, build, and maintain scalable data pipelines and platforms supporting geospatial traffic analytics applications, while ensuring data quality, availability, and efficient processing.

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

Skills

• 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

Qualifications

Must Haves

• 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

Nice to Haves

• 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

Benefits

• Remote work option
• Annual bonus
• Medical insurance
• Vision insurance
• Dental insurance
• 401(k)
• 21 days vacation
• Office lunch provided daily