About
Experience
3-5 years of professional experience in data analytics, business intelligence, or a related role Proven experience working with large, complex datasets in a cloud-based environment Experience supporting enterprise reporting, dashboards, and analytics products Demonstrated experience troubleshooting data quality, pipeline, or ingestion issues
Technical Skills & Languages
Data modeling & transformation
Ability to prepare data optimized for analysis, visualization, and dashboard consumption Ability to write robust code that handles edge cases, implements retries, and fails gracefully under error conditions
SQL
Familiarity with joins, CTEs, window functions, performance optimization Writing and maintaining reusable, well-documented queries
Cloud data platforms
Hands-on experience with Databricks, Azure Synapse, Snowflake, BigQuery, or similar Understanding of how data moves from source systems into cloud storage and analytics layers
Data quality & validation
Ability to identify discrepancies between source systems and cloud datasets Experience facilitating root-cause analysis and coordinating resolution across teams
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) Experience using plotting libraries in a language like Python to create charts and graphs Experience supporting dashboards and reports using tools such as:
Power BI, Tableau, or similar enterprise BI platforms
Soft Skills & Work Style
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 data requests and investigations Preferred Qualifications
Background in Atmospheric Science or another Earth Science Experience with data visualization (Power BI) is a plus Technical Preferences
Experience with PySpark or Python for data analysis and transformations Experience working with GeoTIFFs or other raster datasets 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 Familiarity with ETL / ELT pipelines and orchestration concepts 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
Languages
- English
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