GIS Analyst (QC08804)

About Aereo

Aereo is a deep-tech company that enables enterprises to leverage drone-powered data and geospatial intelligence for better decision-making. We offer an end-to-end solution that spans aerial data capture, processing, and advanced analytics, transforming raw data into accurate maps, 3D models, and actionable insights. Our platform acts as a centralized system for visualizing, analyzing, and managing large-scale operational data, helping organizations replace fragmented, manual workflows with structured, data-driven processes. Working with enterprises across industries such as mining, infrastructure, and urban planning, Aereo bridges the gap between on-ground operations and digital intelligence, enabling greater visibility, efficiency, and control at scale.

About the Role:

The GIS Analyst will be responsible for feature extraction from geospatial data across 2D and 3D datasets, including orthomosaics, elevation models, point clouds, and other imagery, to support Data Science and analytics use cases. This role involves working with complex geospatial datasets and formats, database management, ensuring quality standards, creating and maintaining SOPs, conducting R&D, and collaborating closely with the Data Science team to build AI-assisted tools and workflows.

Key Responsibilities

Perform 2D and 3D digitization of geospatial features on datasets such as orthomosaics, DEMs, point clouds, and satellite imagery.

Annotate both structured features (e.g., solar panel arrays, roof types, and farm boundaries) and highly complex features with significant edge cases (e.g., haul roads, crest toes, coal seams, tree canopies, and other domain-specific assets).

Perform classification and labeling of point cloud datasets from various sources, including drone-based photogrammetry, LiDAR surveys, and terrestrial laser scanners (TLS), across multiple feature categories and use cases.

Create and maintain digitization SOPs covering feature definitions, annotation methodologies, quality standards, edge cases, and domain-specific nuances.

Own the QC process to ensure that digitized datasets are accurate, consistent, complete, and aligned with quality standards.

Work closely with the Data Science team to improve digitization using trained models and AI-assisted annotation workflows.

Conduct R&D on advanced tools for fast-paced, AI-assisted geospatial analysis.

Must-Have Qualifications

Basic understanding of GIS concepts and geospatial data formats, including vector, raster, and point cloud datasets.

Familiarity with tools like QGIS, Global Mapper, Virtual Surveyor, CloudCompare, or similar GIS and point cloud software.

Ability to interpret and annotate geospatial features across domains such as infrastructure, mining, energy, and vegetation.

Ability to understand feature requirements, identify edge cases, and contribute to digitization SOP documentation.

Experience or aptitude in classifying and validating point cloud data from photogrammetry, LiDAR, or TLS sources.

Basic understanding of coordinate reference systems (CRS), map projections, and geospatial data transformations.

Strong attention to detail and commitment to delivering accurate, consistent, and high-quality annotations.

Strong ownership, communication, and a proactive approach to feedback, quality improvement, and efficiency.

Academic qualification: Graduate/Postgraduate in geospatial studies or a related field.

Good-to-Have Qualifications

Python scripting skills for basic automation tasks.

Experience creating technical documentation, standard operating procedures (SOPs), or annotation guidelines.

Familiarity with quality assurance processes, dataset validation methodologies, and annotation metrics.

Familiarity with version control and collaborative tools such as Git and Jira.

Basic SQL or data analysis skills for querying platform databases to find datasets.

Exposure to one or more relevant domains such as mining, infrastructure, utilities, renewable energy, forestry, or agriculture.

Interest in learning emerging geospatial AI, computer vision, and automation technologies.