Data Analytics Analyst 2

“Mindlance • Birmingham, AL

1d

About The Position

The Data Analytics Analyst 2 works with large, complex meteorological datasets in cloud-based environments and supports products from development through operations. The role involves optimizing data processing, troubleshooting data quality and pipeline or ingestion issues, and creating meteorological data visualizations that are understandable to general audiences. The analyst uses SQL and data analysis techniques to build efficient, reusable analytical datasets, while collaborating with data engineers and internal subject-matter experts. The position also requires understanding meteorological variables in traditional numerical weather prediction and AI model output, and identifying variables that may correlate with utility operations.

Requirements

• 3–5 years of professional experience in data analytics or data science leveraging meteorological data sources, such as numerical weather prediction output, satellite imagery, or 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.
• Ability to optimize meteorological data processing to minimize time and memory usage.
• Familiarity with cloud-optimized raster formats such as Zarr.
• Ability to package meteorological output so it is understandable to a general audience.
• Familiarity with static visualizations using Python and/or interactive visualizations using JavaScript.
• Familiarity with SQL joins, CTEs, window functions, and performance optimization.
• Strong understanding of how data structure affects visualization performance and usability.
• Familiarity with bottlenecks when rendering large datasets and ways to address them, such as data binning, downsampling, efficient plotting methods, and memory management.
• Understanding of meteorological variables in traditional numerical weather prediction and AI model output, and ability to identify variables that may correlate with utility operations.
• Strong analytical and problem-solving skills.
• Ability to work independently while collaborating with data engineers and internal subject-matter experts.
• Clear written and verbal communication, especially when explaining data issues or findings.
• Experience prioritizing work across multiple projects.
• Demonstrated attention to detail and a strong focus on data accuracy and reliability.

Nice To Haves

• Background in Atmospheric Science or another Earth Science.
• Experience with Python for data analysis and transformations.
• Experience working with raster datasets in formats such as GRIB, GeoTIFF, and NetCDF.
• Experience with geospatial data and performance optimizations, such as leveraging spatial indices.
• Knowledge of basic statistical concepts such as correlation and trend analysis using least-squares fitting.
• Familiarity with multiple threads and/or CPUs for processing large datasets.
• Experience with version control such as Git.
• 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.

Responsibilities

• Process large meteorological datasets efficiently, optimizing time and memory use.
• Create visualizations of meteorological data for general audiences using static or interactive approaches.
• Write and maintain reusable, well-documented SQL queries.
• Troubleshoot data quality, pipeline, and ingestion issues.
• Guide products from development to operations.
• Identify meteorological variables that may correlate with utility operations.
• Translate business questions into efficient analytical datasets.
• Prioritize work across multiple projects and communicate data issues and findings clearly.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed