Product / Data Engineer (Workflow & Automation)

Role Overview: As a Product / Data Engineer at Urban Intelligence Lab (UIL), you will play a crucial role in leading the automation and productization of data science workflows. Your primary responsibility will involve building scalable pipelines that connect analysis, visualization, and strategic recommendation outputs to ensure efficient, reproducible, and large-scale deployment-ready processes. Key Responsibilities: - Implement and maintain scalable data pipelines for geospatial and mobility workflows. - Convert Python-based proofs of concept (PoCs) and analytical notebooks into production-ready backend code. - Manage and optimize spatial databases (PostgreSQL/PostGIS) for large datasets. - Automate data ingestion, transformation, and reporting processes to streamline delivery. - Collaborate with data scientists to deploy analytical models and services via APIs (FastAPI, Flask). - Maintain and enhance Azure-based infrastructure, including data storage, version control, and CI/CD workflows. - Ensure all code and outputs are well-documented, reproducible, and standardized. - Contribute to internal workflow automation and cloud-native development initiatives. Qualifications and Experience: - Bachelor's or Master's degree in Computer Science, Software Engineering, Geoinformatics, or Data Science. - 3-7 years of professional experience in Python-based data engineering or backend development. - Strong programming skills in Python, with experience using key libraries such as pandas, GeoPandas, SQLAlchemy, and FastAPI/Flask. - Hands-on experience with PostgreSQL/PostGIS for managing and querying spatial data. - Familiarity with cloud environments (preferably Azure) and version control systems (Git). - Solid understanding of ETL concepts, data workflow design, and deployment best practices. - Ability to collaborate with data scientists to translate analytical logic into maintainable, production-ready systems. Desirable Skills: - Experience with workflow orchestration tools (e.g., Airflow, Prefect, or similar). - Knowledge of containerization (Docker) and CI/CD pipelines. - Exposure to big data frameworks (Dask, PySpark, DuckDB, or similar). - Familiarity with API integration and automated reporting or visualization workflows (e.g., Power BI, Jupyter). - Interest or prior experience in urban analytics, mobility data, or spatial data processing.,