GIS Data Analyst at Polaris Digitech Limited

Job Summary

We are seeking a motivated and detail-oriented GIS Data Analyst with expertise in Geographic Information Systems (GIS) and Python programming. The successful candidate will be responsible for collecting, analyzing, and interpreting spatial and non-spatial data to support decision-making processes.

Key Responsibilities

• Collect, clean, and analyze spatial and non-spatial datasets from multiple sources.
• Utilize GIS software (e.g. ArcGIS, QGIS) to perform spatial analysis and create maps and visualizations.
• Develop and maintain Python scripts to automate data processing and analysis tasks.
• Interpret and present data insights through reports, dashboards, and presentations.
• Collaborate with cross-functional teams to support data-driven decision-making.
• Ensure data accuracy, integrity, and consistency across various datasets.
• Conduct geospatial modeling and statistical analysis to address complex problems.
• Identify trends, patterns, and anomalies within datasets and provide actionable insights.
• Document processes and methodologies for reproducibility and knowledge sharing.

Required Skills and Qualifications

• Bachelor\'s degree in Geography, Geology, Data Science, Computer Science, or a related field.
• 2-3 years of experience in data analysis with a focus on GIS and Python programming.
• Proficiency in GIS tools (e.g., ArcGIS, QGIS) and geospatial data processing.
• Strong Python programming skills for data manipulation, automation, and analysis (e.g., Pandas, NumPy, GeoPandas).
• Experience working with spatial databases (e.g., PostGIS, SQLite) and SQL queries. Have practical knowledge of database management.
• Strong analytical and problem-solving skills with attention to detail.
• Excellent communication skills, both written and verbal.

Preferred Skills:

• Knowledge of remote sensing techniques and satellite imagery analysis.
• Familiarity with cloud platforms (e.g Google Cloud) for data storage and processing.
• Experience with web mapping technologies (e.g., Leaflet, Mapbox)
• Understand statistical and machine-learning techniques for geospatial data.