Job Description: [CLIENT] is seeking a Data Engineer with a passion for designing and developing all parts of the data pipeline for large amounts of heterogenous data used in the modeling, simulation, and exploration of data related to urban planning, design, and development. [CLIENT] is a leader in creating models that predict future outcomes of scenarios posed by major regional urban modelers in an effort to build more equitable, sustainable, and enjoyable cities and metropolitan regions to ensure the best use of public funding. Responsibilities: Analyze various land use, parcel, demographic, firmographic, market info, transportation, and spatial data for suitability within our platform Create methodologies and automation to clean, transform, and impute data Work with client teams to help define appropriate schemas for efficient retrieval and storage of data Help manage the data ingestion and preparation system for continuous integration and deployment improvements Qualifications: A readiness to learn and broaden your horizons and is someone that takes initiative and is an active person in coming up with and delivering solutions. An interest, and better yet experience, in the domains that the data relates to Proficiency with Python, creating data pipelines and transformations, working with relational and spatial databases such as Postgres and PostGIS. Bonus if you have had experience with ETL. Experience with Cloud computing, especially Google Cloud Platform, and familiarity with Docker, Kubernetes, Cloud Storage, Dataflow, Cloud Data Fusion, Dataproc, or related Data Lake technologies Familiarity with statistical concepts and practices such as regression modeling, discrete choice, goodness-of-fit evaluation, clustering, and sampling. Understanding CNN, RNN, decision tree and other neural net methodologies a plus A BS or higher degree in Computer Science or data analytics related field, or commensurate experience 2 or more years of industry and programming experience BONUS:Previous experience with geospatial data and experience with data science tools such as Jupyter Notebooks, Pandas, GeoPandas, NumPy, SciPy, scikit-learn, Jax, and TensorFlow