Meteorological Data Analyst
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Meteorological Data Analyst
Technical Skills & Languages
Analytics & Visualization
- 3-5 years of professional experience in data analytics or data science leveraging meteorological data sources (e.g., numerical weather prediction output, satellite imagery, weather station data)
- Proven experience working with large, complex meteorological datasets in a cloud-based environment
- Demonstrated experience guiding a product from development to operations
- Demonstrated experience troubleshooting data quality, pipeline, or ingestion issues
Technical Skills & Languages
- Processing data at scale
- Ability to optimize processing of meteorological data to consume minimal time and memory
- Familiarity with cloud-optimized formats for raster data like Zarr
- Creating meteorological data visualizations
- Ability to package meteorological output in a way that is understandable to a general audience
- Familiarity with static visualizations using Python and/or interactive visualizations using JavaScript
- SQL
- Familiarity with joins, CTEs, window functions, performance optimization
- Writing and maintaining reusable, well-documented queries
Analytics & Visualization
- Strong understanding of how data structure impacts visualization performance and usability
- Familiarity with common bottlenecks when rendering large datasets and their resolution (e.g., data binning, downsampling, efficient plotting methods and memory use)
- Understanding of meteorological variables in traditional numerical weather prediction and AI model output and the ability to identify those that may be correlated with utility operations
- Strong analytical and problem-solving skills
- Ability to work independently while collaborating with data engineers and internal SMEs
- Clear written and verbal communication, especially when explaining data issues or findings
- Experience prioritizing work across multiple projects
- Background in Atmospheric Science or another Earth Science
- Experience with Python for data analysis and transformations
- Experience working with raster datasets in formats like GRIB, GeoTIFF, and NetCDF
- Experience with geospatial data and performance optimizations when working with geospatial data such as leveraging spatial indices
- Knowledge of basic statistical concepts like correlation and trend analysis using methods like least-squares fitting
- Familiarity with the concepts of using multiple threads and/or multiple CPUs to process large datasets quickly
- Experience with version control (Git or similar)
- Basic understanding of data governance, lineage, or metadata management
- Domain & Business Experience
- Experience in utilities, energy, infrastructure, or regulated industries
- Familiarity with operational, asset, or time-series data
- Experience working with imperfect or operational source systems
- Professional Attributes
- Ability to translate business questions into efficient analytical datasets
- Demonstrated attention to detail with a strong focus on data accuracy and reliability