[Remote] Data Analyst (Meteorological & Utility Analytics)

Note: The job is a remote job and is open to candidates in USA. Brooksource is recruiting a Data Analyst to support weather-driven business and operational initiatives through meteorological data analysis, Python development, and analytical product development. The role integrates weather and utility data to improve planning, asset reliability, outage preparedness, and operational decision-making, while supporting enterprise weather analytics platforms and applications.

Responsibilities

• Source, ingest, process, clean, and analyze meteorological datasets from multiple weather data providers and forecasting platforms
• Work with data sources including:
• Numerical Weather Prediction (NWP) models
• Satellite imagery and products
• Weather station observations
• Utility asset and operational datasets
• Integrate weather data with infrastructure data such as substations, circuits, asset inventories, and outage history
• Develop analytical products that help predict, evaluate, and communicate weather-related impacts
• Perform historical weather impact analysis to identify trends, vulnerabilities, and operational opportunities
• Create predictive and forward-looking weather insights that support business decision-making
• Optimize data processing workflows for large-scale meteorological datasets
• Troubleshoot and resolve data quality, ingestion, and processing issues
• Support development, enhancement, and operationalization of enterprise weather applications
• Collaborate with developers, data engineers, meteorologists, and business stakeholders in a remote environment
• Contribute to shared code repositories and established development frameworks using GitHub

Skills

• Bachelor's degree in Meteorology, Atmospheric Science, Data Analytics, Data Science, Earth Science, Computer Science, or a related field
• 3-5 years of professional experience in data analytics, data science, or meteorological data analysis
• Experience working with large and complex weather datasets, including numerical weather prediction output, satellite imagery, and weather station data
• Experience supporting analytical products from development through operational deployment
• Experience troubleshooting data quality, ingestion, and processing issues
• Experience working in collaborative remote development environments
• Strong Python experience for data analysis, transformation, and automation
• Experience processing large-scale datasets efficiently and optimizing performance
• Experience with Databricks or similar cloud analytics platforms
• Strong SQL skills, including: • Complex joins • Common Table Expressions (CTEs) • Window functions • Query optimization • Reusable and well-documented query development
• Experience working with large meteorological datasets and weather model outputs
• Ability to identify, acquire, cleanse, and structure weather data for analysis
• Understanding of meteorological variables and their relationship to operational impacts
• Familiarity with cloud-optimized weather data formats such as Zarr
• Experience creating meaningful data visualizations and analytical products
• Ability to communicate complex weather data to non-technical audiences
• Strong understanding of visualization performance considerations when working with large datasets
• Knowledge of techniques such as: • Data binning • Downsampling • Memory optimization • Efficient plotting methods
• Experience using Git/GitHub for source control and collaborative development
• Familiarity with Agile development methodologies and distributed teams
• Master's degree in Meteorology, Atmospheric Science, or a related discipline strongly preferred
• Graduate-level research, thesis work, or applied meteorological analytics experience is highly valued and may be considered equivalent to professional experience
• Background in Atmospheric Science, Meteorology, or another Earth Science discipline
• Experience working with raster weather datasets including: • GRIB • GeoTIFF • NetCDF
• Experience with geospatial analytics and geospatial performance optimization
• Knowledge of spatial indexing techniques and geospatial data structures
• Familiarity with parallel processing, multi-threading, and distributed computing
• Understanding of statistical concepts including correlation analysis, trend analysis, and least-squares fitting
• Experience with metadata management, data lineage, and data governance concepts
• Experience developing: • Static visualizations using Python • Interactive visualizations using JavaScript frameworks

Benefits

• Remote work

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