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Senior Data Engineer - Geospatial


Engineering & Technology

IT & Telecoms Confidential
1 month ago

Job Summary

We are seeking a highly skilled and motivated Senior-Staff Data/Geospatial Engineer to join our dynamic team. In this role, you will play a pivotal role in the transformation and management of geospatial data, ensuring its accuracy, availability, and usability for a variety of applications. You will collaborate closely with cross-functional teams to develop data models, optimize spatial operations, and contribute to the development of customer-facing APIs.

  • Minimum Qualification: Degree
  • Experience Level: Senior level
  • Experience Length: 5 years

Job Description/Requirements

A bit about you 

  • Bachelor's or Master's degree in Computer Science, Geospatial Science, or related field.

  • Proven experience in geospatial data management and transformation.

  • Strong background in data engineering, including data lake and pipeline management (5+ years).

  • Proficiency in databases such as Postgres and PostGIS.

  • Exceptional knowledge of data modeling and spatial concepts.

  • Proficiency in SQL and Python; knowledge of Spark, Typescript and JavaScript is a plus.

  • Experience in spatial optimization and customer-facing API development is advantageous.

  • Excellent problem-solving skills and a keen attention to detail.

  • Effective communication and team management skills.

About your role at Ready 

  • Geospatial Data Modeling:

    • Collaborate closely with the frontend team, product team, and geospatial data scientist to understand the challenge and provide data modeling solutions.

    • Ensure data models are adaptable to evolving project needs and future scalability.

    • Design and optimize database schemas to support spatial-temporal queries and analyses.

    • Implement data indexing and partitioning strategies for improved query performance.

  • Data Transformation and Ingestion:

    • Execute data transformation processes to convert raw geospatial data into structured formats.

    • Orchestrate the data transformation and ingestion workflow with Airflow.

    • Set up scheduled data transformation and ingestion jobs for scalability.

  • Data Storage and Maintenance:

    • Implement and maintain data lake and data warehouse architecture for efficient storage and retrieval of geospatial data.

    • Design different strategies for optimizing the spatial-temporal data storage, update, and backup. 

    • Design and implement best practice with cloud solutions: AWS and GCP.

  • Spatial Data Expertise:

    • Apply expertise in static tiling, dynamic tiling, and raster tiles to optimize the retrieval and rendering of geospatial data.

    • Maintain the tileserver for performance and reliability.

    • Implement spatial indexing and caching strategies to accelerate spatial data access.

  • Data Verification:

    • Develop automated data verification pipelines to validate the quality and consistency of geospatial datasets.

    • Leverage statistic and data visualization tools to validate the data or identify data abnormalities.

    • Implement data cleansing and transformation routines to correct errors and inconsistencies.

    • Monitor data verification processes and promptly address issues to maintain data reliability.

  • Programming languages:

    • Utilize SQL and Python to perform data manipulation, transformation, and analysis tasks.

    • Develop custom scripts and functions to automate geospatial data processing workflows.

    • Develop custom packages for data engineer team to share.

    • Stay updated on the latest updates in cloud SQL and Python for data engineering.

  • Customer-Facing API Development:

    • Contribute to the development of customer-facing APIs that expose geospatial data and services.

    • Ensure API endpoints are well-documented, secure, and efficient in data retrieval and delivery.

    • Collaborate with frontend developers to integrate APIs seamlessly into applications.

  • Machine Learning, Optimization and AI - From Solution to Production:

    • Collaborate with domain experts to understand specific spatial challenges and devise effective solutions.

    • Solve complex spatial optimization problems, such as route planning and network planning, to enhance geospatial capabilities.

    • Work on integrating machine learning or AI frameworks into production environments to leverage geospatial data.

    • Collaborate with data scientists and AI engineers to deploy models that enhance geospatial capabilities.

    • Ensure the scalability and reliability of AI-driven solutions for geospatial applications.

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