Data Engineer

Unlocking the full potential of forests, both ecologically and emotionally.

Job Overview

We’re looking for a skilled Data Engineer to design, build, and maintain robust data pipelines and infrastructure focused on geospatial and temporal datasets. You’ll play a key role in processing complex environmental data, enabling real-time analytics, and supporting our mission to create a greener planet. If you’re passionate about data systems, geospatial technology, and sustainability, we’d love to hear from you!

Key Responsibilities

• Data Pipeline Development: Design and implement scalable ETL/ELT pipelines to ingest, process, and transform geospatial and temporal data from diverse sources (e.g., satellite imagery, IoT sensors, historical records).
• Geospatial Data Management: Optimize storage and querying of geospatial datasets using tools like PostGIS, GeoPandas, or similar technologies.
• Temporal Data Processing: Build systems to handle time-series data, ensuring efficient analysis of trends and patterns over time.
• Infrastructure Optimization: Develop and maintain data architectures (e.g., data lakes, warehouses) on cloud platforms to support high-volume geospatial and temporal workloads.
• Collaboration: Partner with data scientists and product teams to deliver clean, reliable datasets for visualization, machine learning, and decision-making.
• Performance Tuning: Monitor and enhance the performance of data systems, ensuring low-latency access to geospatial and temporal insights.
• Data Quality & Security: Implement processes to ensure data accuracy, consistency, and compliance with privacy standards.

Required Skills & Qualifications

• Experience: 3+ years in data engineering or a related role, with hands-on experience in geospatial and/or temporal data processing.
• Programming: Proficiency in Python, SQL, and familiarity with languages like Java or Scala.
• Geospatial Tools: Experience with geospatial libraries (e.g., GDAL, GeoPandas, QGIS) and databases (e.g., PostGIS).
• Data Technologies: Knowledge of big data frameworks (e.g., Apache Spark, Kafka) and time-series databases (e.g., InfluxDB, TimescaleDB).
• Cloud Expertise: Practical experience with cloud platforms (AWS or Azure) and their geospatial/temporal data services (e.g., BigQuery GIS, S3).
• Problem-Solving: Strong analytical skills to tackle complex data challenges.
• Education: Bachelor’s degree in Computer Science, Engineering, GIS, or a related field (or equivalent experience).

Nice-to-Haves

• Experience with environmental or sustainability-focused datasets.
• Familiarity with real-time data streaming and processing.
• Contributions to open-source geospatial or data engineering projects.

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