Top 12 GIS Analyst Skills to Put on Your Resume

In today’s crowded hiring lanes, the right skills jump off the page. GIS Analysts who foreground the tools and thinking that drive real spatial insight land interviews faster. Below, a focused set of 12 capabilities that deserve space on your resume—practical, current, and tuned for the real workflows of Geographic Information Systems.

GIS Analyst Skills

1. ArcGIS
2. QGIS
3. Python
4. SQL
5. R
6. JavaScript
7. AutoCAD
8. Remote Sensing
9. CARTO (formerly CartoDB)
10. PostgreSQL
11. ERDAS IMAGINE
12. FME

1. ArcGIS

ArcGIS (with ArcGIS Pro at the core) is a full-stack system for creating, managing, analyzing, and sharing spatial data. Analysts lean on it for polished maps, robust geoprocessing, and collaboration across web and desktop.

Why It's Important

It delivers end-to-end spatial analysis, strong data management, and clear visualization in one ecosystem—essential when projects demand repeatability, scale, and clean outputs.

How to Improve ArcGIS Skills

Go beyond point-and-click. Build fluency that pays off:

1. Master ArcGIS Pro: Use ModelBuilder for repeatable workflows, Arcade for smart labeling/expressions, and geoprocessing histories for auditability.
2. Automate with Python: Dive into ArcPy and the ArcGIS API for Python to batch tasks, schedule jobs, and wrangle services.
3. Expand to the web: Work with ArcGIS Online, Experience Builder, and Dashboards to publish data, build interactive apps, and track key metrics.
4. Adopt spatial data science habits: Blend tools like kernel density, suitability modeling, and Space Time Pattern Mining with thoughtful cartography.
5. Stay current: New Pro releases ship often. Skim release notes, test features, fold the wins into your workflow.
6. Engage the community: The Esri Community (formerly GeoNet) is a goldmine for tips, patterns, and troubleshooting.
7. Validate your skills: Esri certifications help signal depth on complex environments.

Sharpening these areas turns everyday tasks into streamlined, documented, and shareable workflows.

How to Display ArcGIS Skills on Your Resume

2. QGIS

QGIS is a powerful, open-source GIS for analysis, editing, and mapmaking. It’s fast, flexible, and extensible through plugins and Python.

Why It's Important

It gives analysts enterprise-grade capability without licensing friction, promotes open standards, and blends smoothly with geospatial stacks like PostGIS and GeoPackage.

How to Improve QGIS Skills

Grow from quick fixes to robust tooling:

1. Lock down the fundamentals: Master symbology, joins, projections, and the Processing toolbox.
2. Automate with PyQGIS: Write custom scripts and tools. Save hours. Build consistency.
3. Lean on plugins: Profile, vet, and standardize a plugin set (e.g., QuickMapServices, Profile Tool, Mergin Maps).
4. Modeler for repeatability: Create Processing models to standardize multi-step tasks.
5. Expressions everywhere: The Field Calculator and expressions engine unlock smart data transformations.
6. Contribute or document: Improving docs, translations, or bug reports deepens understanding and helps the community.

With a strong QGIS toolkit, you can move fast and still produce defensible work.

How to Display QGIS Skills on Your Resume

3. Python

Python is the spine of modern GIS automation—scripting geoprocessing, munging data, building services, and stitching together entire ETL pipelines.

Why It's Important

It transforms long, manual tasks into reliable, shareable workflows and unlocks richer analysis with spatial libraries and notebooks.

How to Improve Python Skills

Build practical muscle memory:

1. Core syntax, tested: Strengthen control flow, data structures, packaging, and environments.
2. Key spatial libs: GeoPandas, Shapely, Fiona, Rasterio, PyProj, rtree, xarray, and rasterstats cover most daily needs.
3. Visualization: Matplotlib, Plotly, and Folium for maps; Altair for quick exploration.
4. ArcPy/PyQGIS: Tie scripts to your desktop stack for end-to-end automation.
5. Projects over tutorials: Use real datasets (OpenStreetMap extracts, Natural Earth, local open data portals) and ship small tools.
6. Testing and logging: Add pytest and proper logging to make your scripts robust and debuggable.
7. Share: Publish gists or packages; feedback sharpens code quality.

Keep building. The right habits pay compounding returns.

How to Display Python Skills on Your Resume

4. SQL

SQL is the language of spatial databases. It extracts, filters, aggregates, and morphs geographic data right where it lives.

Why It's Important

Spatial SQL lets you answer complex location questions quickly—joins, overlays, buffers, and proximity—without dragging everything into desktop tools.

How to Improve SQL Skills

Think spatial first, and fast:

1. Spatial functions: Get fluent with ST_Intersects, ST_Contains, ST_DWithin, ST_Distance, ST_Union, ST_Buffer, ST_Transform, and ST_MakeValid.
2. Indexing: Build and maintain GiST or SP-GiST indexes for geometry/geography columns. Understand when BRIN helps for very large, ordered data.
3. Write tighter queries: Use CTEs, window functions, filtered aggregates, and selective column retrieval. Avoid SELECT * in production.
4. Explain plans: Use EXPLAIN/EXPLAIN ANALYZE to diagnose slowdowns. Fix with indexes, constraints, or query rewrites.
5. Handle big data: Partition large tables, materialize views for heavy reads, and schedule VACUUM/ANALYZE.
6. Practice on real problems: Road-network nearest facilities, parcel-overlay counts, drive-time aggregations—then benchmark.

Once your spatial SQL clicks, analysis gets crisp and repeatable.

How to Display SQL Skills on Your Resume

5. R

R shines for statistical and spatial modeling, reproducible analysis, and elegant visualization—especially when paired with modern spatial packages.

Why It's Important

It bridges GIS with statistics, forecasting, and advanced modeling, enabling robust inference and publishable analysis.

How to Improve R Skills

Use the current spatial ecosystem (and avoid retired packages):

1. Modern spatial stack: Focus on sf (vector), terra and stars (raster), exactextractr (zonal stats), and tmap/ggplot2/leaflet for maps.
2. Data pipelines: dplyr, tidyr, and data.table keep transformations tidy and fast.
3. Reproducibility: Quarto/R Markdown for reports; renv for environment management; targets for pipeline orchestration.
4. Modeling: Use parsnip, tidymodels, or mgcv for spatial/regression work; spdep and sfnetworks for spatial dependence and networks.
5. Practice with real datasets: Census extracts, environmental rasters, transportation feeds—build end-to-end scripts.
6. Keep up: The R spatial ecosystem evolves quickly; skim changelogs and vignettes.

Note: rgdal, rgeos, and sp have been superseded—prefer sf/terra for new work.

How to Display R Skills on Your Resume

6. JavaScript

JavaScript powers interactive web maps and lightweight spatial tools that live in the browser.

Why It's Important

It lets you put analysis in users’ hands—fast, visual, and accessible—so decisions don’t wait on exports and emails.

How to Improve JavaScript Skills

Write cleaner, faster map apps:

1. Get the essentials right: ES6+, modules, fetch, async/await, and array methods.
2. Map libraries: Learn Leaflet for simplicity; OpenLayers and MapLibre GL JS for advanced rendering; deck.gl for high-performance layers.
3. TypeScript: Add types for safer, maintainable code on bigger apps.
4. State and components: Keep code organized with components (vanilla patterns or frameworks when appropriate).
5. Debugging: Live in DevTools—breakpoints, network panels, performance tracing.
6. Performance: Minify payloads, debounce events, tile/vector tiling strategies, and web workers for heavy computations.
7. Data handling: Stream GeoJSON, handle MVT, and cache responses smartly.

Your goal: smooth interactions at scale, even with chunky datasets.

How to Display JavaScript Skills on Your Resume

7. AutoCAD

AutoCAD is precision drafting for 2D/3D design. In GIS work, it’s often the bridge between engineering drawings and geospatial datasets.

Why It's Important

Infrastructure projects, utility plans, and site designs flow through CAD. Converting, aligning, and cleaning those drawings for GIS analysis is a core skill.

How to Improve AutoCAD Skills

Make CAD-to-GIS less painful, more exact:

1. Map 3D toolset: Connect to spatial data sources, manage coordinate systems, and export georeferenced outputs.
2. Automation: Use scripts, Action Macros, and AutoLISP to batch-clean layers, blocks, and attributes.
3. Standards: Create templates with consistent layers, naming, and metadata fields for reliable conversions.
4. Coordinate systems: Practice assigning and transforming projections correctly (avoid silent misalignments).
5. Plugins: Add-ons can streamline import/export and validation between DWG/DXF and GIS formats.
6. QA routines: Snap checks, topology rules, and geometry validation reduce downstream rework.

Clean CAD in, clean GIS out.

How to Display AutoCAD Skills on Your Resume

8. Remote Sensing

Remote sensing collects information about Earth from satellites, aircraft, and drones—optical, thermal, radar, and beyond.

Why It's Important

It provides timely, large-area coverage for land cover, vegetation health, urban growth, disaster response, and environmental monitoring.

How to Improve Remote Sensing Skills

Turn imagery into answers:

1. Know your sensors: Landsat 9 and Sentinel-2 for multispectral, Sentinel-1 for SAR, and commercial sources for higher resolution.
2. Preprocessing: Get comfortable with atmospheric correction, cloud masking, coregistration, and resampling.
3. Indices and composites: NDVI, NDWI, NBR, EVI, and temporal mosaics to track change.
4. Classification: Practice supervised/unsupervised methods, accuracy assessment, and post-classification cleanup.
5. Time series: Detect trends and anomalies with stacked rasters and robust statistics.
6. Cloud platforms: Explore at-scale analysis with platforms such as Google Earth Engine or Sentinel Hub for rapid prototyping.
7. Python/R toolchains: Rasterio, xarray, scikit-image, and terra/stars for processing pipelines.

Good remote sensing blends physics, stats, and careful validation.

How to Display Remote Sensing Skills on Your Resume

9. CARTO (formerly CartoDB)

CARTO is a cloud-native spatial platform for storing, analyzing, and publishing interactive maps and apps.

Why It's Important

It accelerates data sharing and live analysis across teams—no heavy desktop installs—making location insights easy to explore and communicate.

How to Improve CartoDB Skills

Make your projects sharper, faster:

1. SQL and PostGIS: Write spatial queries to power dynamic layers, filters, and aggregates.
2. Data Observatory: Enrich projects with demographic, mobility, and environmental context.
3. Visual design: Use smart styling, animated layers, and custom basemaps for clarity and impact.
4. Builder/Workspace: Assemble analyses quickly with low-code tools, then extend with custom code where needed.
5. Integration: Connect external warehouses and APIs; schedule refreshes for always-fresh maps.

Clarity wins—let the map tell the story instantly.

How to Display CartoDB Skills on Your Resume

10. PostgreSQL

PostgreSQL is a rock-solid relational database. With PostGIS, it becomes a spatial powerhouse for vector and raster operations.

Why It's Important

It centralizes geospatial data, enables complex analysis with SQL, and scales to serious workloads—crucial for multi-user, production-grade GIS.

How to Improve PostgreSQL Skills

Think performance, reliability, and scale:

1. PostGIS essentials: Geometry vs. geography, valid geometries, projections, and raster support (including postgis_raster).
2. Indexes and constraints: GiST/SP-GiST for spatial, B-tree for attributes; add constraints to protect data integrity.
3. Partitioning: Range or list partitioning for massive tables; prune scans and speed up queries.
4. Tuning: Adjust shared_buffers, work_mem, maintenance_work_mem, effective_cache_size, and parallelism.
5. Query discipline: Use EXPLAIN ANALYZE, avoid unnecessary casts, pre-aggregate where possible, and cache results with materialized views.
6. Maintenance: Routine VACUUM/ANALYZE/AUTOVACUUM checks; monitor bloat and slow queries.
7. Operations: Connection pooling (e.g., PgBouncer), backups, and version upgrades to keep pace with improvements.

The payoff is a responsive database that handles spatial questions without breaking a sweat.

How to Display PostgreSQL Skills on Your Resume

11. ERDAS IMAGINE

ERDAS IMAGINE is a mature platform for remote sensing and image analysis—classification, change detection, feature extraction, and more.

Why It's Important

When satellite or aerial data drives the project, IMAGINE offers deep, specialized tooling built for accuracy and repeatability.

How to Improve ERDAS IMAGINE Skills

Focus on the full pipeline:

1. Interface fluency: Know the toolboxes for preprocessing, classification, and mosaicking.
2. Grounded classification: Train with representative samples; validate with confusion matrices and field truth.
3. Feature extraction: Tune segmentation parameters and rulesets for crisp vector outputs.
4. Workflows you can rerun: Save models/macros that standardize steps across projects.
5. Real-world datasets: Practice with varied sensors and resolutions to understand trade-offs.
6. Keep learning: Product updates and user communities surface new methods and shortcuts.

The more you iterate with real imagery, the faster you’ll reach reliable classifications.

How to Display ERDAS IMAGINE Skills on Your Resume

12. FME

FME (Feature Manipulation Engine) is a no/low-code workbench for transforming, validating, and moving spatial and tabular data across systems.

Why It's Important

It turns messy, multi-format inputs into clean, automated pipelines—exactly what busy GIS teams need for dependable data flows.

How to Improve FME Skills

Build smarter, sturdier workspaces:

1. Core transformers: Get fluent with Tester, AttributeManager, GeometryValidator, Dissolver, Aggregator, and Reprojector.
2. Schema discipline: Validate fields, types, and coordinate systems early in the flow to avoid surprises later.
3. Performance: Use parallel processing, feature caching strategically, and bookmarks to structure complex graphs.
4. Custom transformers: Wrap repeated patterns; version them; document assumptions.
5. Automations and Server: Schedule jobs, event-trigger pipelines, and notifications for hands-off ETL.
6. Best practices: Clear naming, annotations, parameterized inputs, and error handling make handoffs painless.

With FME humming, brittle manual steps disappear.

How to Display FME Skills on Your Resume