Meteo Data Engineer

at  Dexter Energy

Dexter Energy is an Amsterdam-based scale-up developing AI-powered forecasting and optimization solutions to help renewable energy companies navigate short-term power markets. Our mission is to accelerate the transition to a sustainable and affordable energy system, moving away from fossil fuels toward a future powered by renewables.

MINIMUM QUALIFICATIONS:

• Bachelor’s or higher degree in Computer Science, Meteorology, Engineering or a quantitative field e.g. Physics.
• Proven experience in data engineering or software engineering, with a strong focus on processing and managing array data.
• Proficiency with Python and its data and machine learning ecosystem (e.g., NumPy, pandas, xarray).
• Experience with multi-dimensional array processing and high-performance data pipelines.
• Good knowledge of continuous integration and deployment (CI/CD).
• Strong software engineering and problem-solving skills.
• Excellent communication skills: ability to clearly and succinctly explain complex technical concepts.

BONUS QUALIFICATIONS (LEARNING OPPORTUNITIES):

• Familiarity with environmental datasets such as weather forecasts and measurements, satellite imagery, and AI-based weather forecasting models.
• Knowledge of geospatial data tooling and modelling such as NetCDF, GRIB, and multi-dimensional array technologies such as TileDB and Zarr.
• Experience with Google Cloud Platform and Gitlab.

ABOUT THIS ROLE

This position is part of the Meteo team within our Tech department, working closely with cross-functional experts in data science, meteorology, and engineering. You’ll collaborate with Giuseppe (Data Science Manager) and Yuxi, Fons, Igor, Yang (Meteo Data Engineers) to enhance the precision of our renewable energy forecasting and trading products.
We’re seeking an experienced Meteo Data Engineer to join our team. In this role, you’ll harness meteorological data to refine and optimize our forecasting and trading solutions. You’ll work alongside meteorology and data science experts, securing the reliability and significance of our meteorological data sources.

KEY RESPONSIBILITIES

• Develop and maintain our in-house, high-performance weather data library built on TileDB.
• Optimize and expand our high-performance data processing codebase using Python libraries such as NumPy, pandas, scikit-learn, xarray, and Polars.
• Operate and manage data orchestration and scheduling tools like Airflow and Kubernetes.
• Research and integrate new meteorological data sources, enhancing the accuracy and reliability of our forecasts.
• Build and maintain data pipelines (terabytes of multi-dimensional meteorological data) for nowcasting and in-house forecasts.