GIS Analyst - Data Solutions

About Azira

Azira is a data-first media and insights company on a mission to reinvent how brands use data to make smarter decisions, from where to open their next location to how they connect with customers in the real world. We blend marketing, location analytics, and strategy into a single platform, helping leading brands take action with confidence. We move fast, think boldly, and care deeply about building things that matter.

Why This Role Matters

This role will contribute to building and maintaining high-quality geospatial data that supports Azira’s Places and Geospatial Data products. The work involves processing large-scale geospatial datasets, building reliable data pipelines, and ensuring the accuracy and usability of location-based data for production use.

The role will also contribute to improving how geospatial data is extracted, processed, and analyzed using GIS tools, Python, PySpark, SQL, and AWS.

What you’ll do

• Work with large-scale geospatial datasets, including POIs, polygons, boundaries, roads, buildings, and other spatial datasets.
• Use QGIS or ArcGIS for spatial data visualization, analysis, editing, validation, and quality checks.
• Extract and process geospatial data from multiple sources and formats, preparing datasets for downstream processing and production use.
• Develop and maintain ETL/data processing pipelines for geospatial data ingestion, transformation, enrichment, validation, and export.
• Develop Python-based automation for GIS and data-processing workflows and use PySpark/Spark to efficiently process large-scale geospatial datasets.
• Perform spatial analysis and operations, including spatial joins, polygon and geometry operations, intersection/overlap analysis, spatial filtering, geospatial enrichment, and spatial indexing.
• Write efficient SQL queries for data extraction, transformation, validation, and analysis, and work with geospatial formats such as Shapefile, GeoJSON, KML/KMZ, CSV, GeoPackage, and geodatabases.
• Perform data quality and validation checks across spatial data sources, inputs, and outputs, identifying issues such as invalid geometries, duplicate records, missing attributes, incorrect coordinates, and topology errors.
• Work with AWS and geospatial databases/data platforms to store, process, manage, and support large-scale datasets used by downstream applications.
• Collaborate with Data Analysts, Automation Specialists, Media, Account, and Solutions teams to support GIS-related requirements and integrate geospatial insights into broader analyses.
• Support requests from internal teams by understanding requirements, communicating findings clearly, and escalating questions or issues when required.
• Document data sources, assumptions, extraction methodologies, validation procedures, and data-processing workflows to ensure consistency and maintainability.

What you Bring

• Strong hands-on experience with QGIS or ArcGIS, with a solid understanding of GIS concepts and spatial data processing.
• Strong Python, PySpark/Spark, and SQL skills for data processing, automation, analysis, and transformation.
• Experience building and maintaining ETL/data pipelines for geospatial data extraction, transformation, enrichment, and validation.
• Experience working with AWS and large-scale datasets, along with a good understanding of common geospatial formats such as Shapefile, GeoJSON, KML/KMZ, CSV, and GeoPackage.
• Strong analytical and problem-solving skills, with a focus on data quality, accuracy, and following reliable QA processes.
• Experience with Apache Sedona/GeoSpark, PostGIS/PostgreSQL, GDAL/OGR, or similar geospatial technologies.
• Familiarity with Elasticsearch/OpenSearch, spatial indexing, H3, REST APIs, or geospatial APIs.
• Experience working with large-scale POI/location intelligence datasets and geospatial geometry or topology validation.
• Experience with AWS S3, EMR, or similar large-scale data-processing environments.
• A continuous improvement mindset, with an interest in improving workflows, reducing manual effort, and standardizing repeatable processes.
• Willingness to learn, seek feedback, document processes, and contribute to knowledge sharing and consistent team execution.