Data Analytics Analyst

Experience

• 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

• 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

• 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

• Experience in utilities, energy, infrastructure, or regulated industries
• Familiarity with operational, asset, or time-series data
• Experience working with imperfect or operational source systems

• Ability to translate business questions into efficient analytical datasets
• Demonstrated attention to detail with a strong focus on data accuracy and reliability