GIS Analyst - Data Solutions

Azira; Bengaluru; On-site; INR 1,250,000 - 1,850,000; Full time

Job summary

Azira is seeking a Geospatial Data Engineer to build and maintain high-quality geospatial datasets for Places and Geospatial Data products. The role involves processing large-scale datasets, creating reliable data pipelines, and ensuring dataset accuracy for production use.

You will work with GIS tools (QGIS/ArcGIS), Python, PySpark, SQL, and AWS to extract, process, and analyze spatial data, supporting cross-functional teams and ensuring robust QA.

Qualifications

• Experience with GIS concepts and spatial data processing.
• Strong Python, PySpark, and SQL skills for data workflows.
• Experience building ETL/data pipelines for geospatial data.
• Familiarity with AWS and large-scale datasets.
• Proven focus on data quality and QA processes.

Responsibilities

• Work with large-scale geospatial datasets (POIs, polygons, roads, buildings).
• Use QGIS or ArcGIS for visualization, analysis, editing, and validation.
• Extract and process geospatial data from multiple sources for production use.
• Develop and maintain ETL/data pipelines for ingestion, transformation, and validation.
• Develop Python-based automation for GIS workflows and use PySpark for large-scale processing.
• Perform spatial analysis including joins, topology checks, and indexing.
• Write SQL queries for data extraction, transformation, validation, and analysis with geospatial formats.
• Coordinate with AWS and geospatial databases/data platforms for storage and processing.

Skills

GIS concepts

Python

PySpark

SQL

Data processing

Data quality

AWS

Analytical thinking

Tools

QGIS

ArcGIS

PostGIS

GDAL/OGR

Sedona/GeoSpark

Elasticsearch/OpenSearch

Job description

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.