Data Analyst (Meteorological & Utility Analytics)

Brooksource; United States; Remote; USD 90,000 - 120,000; Full time

Job summary

Brooksource is seeking a Data Analyst with a strong background in meteorological data, analytics, and Python development to support weather-driven business initiatives. This role sits at the intersection of meteorology, data science, and utility operations, helping turn weather intelligence into actionable insights for planning and outage preparedness.

You will source and harmonize large weather datasets, build analytical products, and collaborate with developers, data engineers, and

Qualifications

• Bachelor’s degree in Meteorology, Atmospheric Science, Data Analytics, Data Science, Earth Science, Computer Science, or a related field.
• Master’s degree in Meteorology, Atmospheric Science, or a related discipline strongly preferred.
• Graduate-level research, thesis work, or applied meteorological analytics experience is valued.

Responsibilities

• Source, ingest, process, clean, and analyze meteorological datasets from multiple weather data providers and forecasting platforms.
• Develop analytical products that help predict, evaluate, and communicate weather-related impacts.
• Collaborate with developers, data engineers, meteorologists, and business stakeholders in a remote environment.

Skills

Python

Databricks

CTEs

Window functions

Query optimization

GitHub

Agile

Cloud analytics platforms

Data visualization

Education

Bachelor's degree in Meteorology/Atmospheric Science/Data Analytics/Data Science/Earth Science/CS

Master's degree in Meteorology/Atmospheric Science/related field

Tools

GitHub

Databricks

Job description

Data Analyst (Meteorological & Utility Analytics)

Location: Remote

Duration: 3-Year Contract

Employment Type: Long-Term Contract

Hours: Standard business hours (approximately 9:00 AM - 5:00 PM)

We are seeking a Data Analyst with a strong background in meteorological data, analytics, and Python development to support critical weather-driven business and operational initiatives. This role sits at the intersection of meteorology, data science, and utility operations, helping transform weather intelligence into actionable insights that improve planning, asset reliability, outage preparedness, and operational decision-making.

The ideal candidate will have experience working with large-scale weather datasets, building analytical products, and developing data-driven insights that support business and operational stakeholders. This position will play a key role in developing and enhancing enterprise weather analytics platforms and applications.

Key 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.

Required Qualifications

Education

• Bachelor's degree in Meteorology, Atmospheric Science, Data Analytics, Data Science, Earth Science, Computer Science, or a related field.
• 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.

Experience

• 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.

Technical Skills

• 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.
• Common Table Expressions (CTEs)
• Window functions
• Query optimization
• Reusable and well-documented query development

Meteorological Data Processing

• 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.

Preferred Qualifications

• 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.
• Static visualizations using Python