Job Openings M16 - Data Engineer

About the job M16 - Data Engineer

Overview

Build and maintain scalable data pipelines and platforms to support data-driven applications and analytics.

Key Responsibilities

    • Design, develop and deploy data tables, views and marts in data warehouses, operational data store, data lake and data virtualization.
    • Perform data extraction, cleaning, transformation, and flow.
    • Design, build, launch and maintain efficient and reliable large-scale batch and real-time data pipelines with data processing frameworks.
    • Integrate and collate data silos in a manner which is both scalable and compliant
    • Collaborate with Product Manager, Data Architect, Business Analysts, Frontend Developers, Designers and Data Analyst to build scalable data-driven products.
    • Be responsible for developing backend APIs & working on databases to support the applications.
    • Work in an Agile Environment that practices Continuous Integration and Delivery.
    • Work closely with fellow developers through pair programming and code review process.

Key Requirements

  • Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field
  • Minimum 4 years of experience in data engineering, data platform development, or related roles
  • Strong experience in data engineering and pipeline development
  • Proficiency in databases, SQL, and data processing frameworks
  • Experience with cloud data platforms
  • Familiarity with Agile and CI/CD practices
  • Familiar with GIS platforms: ArcGIS Server, PostGIS
  • Able to use spatial Python libraries: GeoPandas, Shapely
  • Build scalable geospatial data pipelines for ingestion, transformation, storage, and quality checks from multiple government sources.
  • Implements data cataloging, metadata, lineage, and security; optimizes storage and performance for GIS analytics (e.g., PostGIS, data lakes).
  • Provides reliable data services and contracts to support downstream GIS analytics and front-end visualizations, collaborating closely with GIS and frontend teams.
  • Data management: applies validation, cleansing, reprojection, metadata, and data lineage of geospatial data

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