Data Engineer II - Street Data

Overview

We are seeking a talented Data Engineer to join our team and play a key role in building world-wide street networks. You will be responsible for designing, developing, and maintaining ETL processes to transform street and roadway data into formats optimized for Esri’s core transportation network analysis and routing applications used by millions around the world.

Responsibilities

• Design, implement, and maintain scalable ETL pipelines for ingesting, transforming, and loading street data from various sources


• Automate, maintain, and optimize data processing workflows to ensure timely and accurate updates to transportation network datasets


• Collaborate with data providers, customers, and software engineers to understand data requirements and deliver high-quality solutions


• Develop and maintain processes to analyze routing requests for data-related errors and collaborate with data providers to ensure issues are corrected in future data updates


• Document data models, workflows, and best practices

Requirements

• 2+ years of experience building and maintaining ETL pipelines


• Proficiency in Python, SQL, and data-processing frameworks or orchestration tools (such as Apache Airflow, Dagster)


• Strong understanding of relational databases (such as PostgreSQL, SQL Server)


• Experience with containerization (such as Docker) and CI/CD pipelines


• Familiarity with version control systems (such as Git)


• Excellent problem-solving and communication skills


• Bachelor’s degree in Computer Science, Data Engineering, GIS, or related field

Recommended Qualifications

• Experience with geospatial data formats


• Experience with cloud data platforms (such as AWS, Azure) and cloud-based ETL tools


• Exposure to big data technologies (such as Spark, Hadoop)


• Knowledge of transportation network modeling and routing algorithms


• Familiarity with APIs for street data (such as OpenStreetMap, HERE, TomTom)


• Understanding of data governance, quality, and security best practices

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