CVE Data Visualization Dashboard Development
Budget: $250 – $750 AUD
Job Title:
Build a CVE Dashboard Platform (Elasticsearch + Superset/D3.js + Python Integration)
Job Description:
We are looking for a full-stack data visualization developer to help build an interactive dashboard. The project involves parsing and preprocessing JSON data, indexing it into Elasticsearch, and building visual dashboards using Apache Superset. Optionally, Kibana or D3.js can be used for some charts if Superset is limiting.
This project is for internal educational and reporting use. The dashboards should be embeddable into an existing website (iframe or through a Flask reverse proxy).
Scope of Work:
1. Data Integration
Download JSON CVE data (~200,000+ records)
Design efficient Elasticsearch indices and mappings.
Ingest the JSON data into Elasticsearch.
2. Python Middleware
Write Python scripts or APIs to preprocess and clean the raw JSON.
Develop search endpoints using Elasticsearch queries (e.g., filter by Vendor, Product, Severity).
Output processed JSON that can be consumed by the frontend/dashboard.
3. Dashboard Development Use Superset (preferred) or Kibana to create dashboards.
Types of charts needed:
Bar charts
Pie charts
Date histograms
Heatmaps
Data tables
Treemaps
4. Frontend Embedding
Dashboards should be embeddable on a website using iframe or served via Flask and NGINX.
Custom visuals using D3.js or Chart.js if Superset is too limited.
Filters: Dropdown filters for vendor, product, impact, year, etc.
Deliverables:
Working Elasticsearch setup (Docker or Elastic Cloud)
Python scripts for preprocessing and queries
Superset dashboard exports or backups
All source code and deployment instructions (GitHub or GitLab repo)
This project will be hosted internally.
Required Skills:
Python (Pandas, Elasticsearch-py, Flask or FastAPI)
Elasticsearch (data modeling, indexing, querying)
Superset (dashboarding and filters) or Kibana
Optional: D3.js or Chart.js for advanced charts
Docker, NGINX, and reverse proxy configuration
Familiarity with Git
How to Apply:
Share links or screenshots of dashboards or visual data projects you've done before.
Mention your experience with Superset, D3.js, or Elasticsearch.
Describe briefly how you would set this up if it were hosted on a website.
Build a CVE Dashboard Platform (Elasticsearch + Superset/D3.js + Python Integration)
Job Description:
We are looking for a full-stack data visualization developer to help build an interactive dashboard. The project involves parsing and preprocessing JSON data, indexing it into Elasticsearch, and building visual dashboards using Apache Superset. Optionally, Kibana or D3.js can be used for some charts if Superset is limiting.
This project is for internal educational and reporting use. The dashboards should be embeddable into an existing website (iframe or through a Flask reverse proxy).
Scope of Work:
1. Data Integration
Download JSON CVE data (~200,000+ records)
Design efficient Elasticsearch indices and mappings.
Ingest the JSON data into Elasticsearch.
2. Python Middleware
Write Python scripts or APIs to preprocess and clean the raw JSON.
Develop search endpoints using Elasticsearch queries (e.g., filter by Vendor, Product, Severity).
Output processed JSON that can be consumed by the frontend/dashboard.
3. Dashboard Development Use Superset (preferred) or Kibana to create dashboards.
Types of charts needed:
Bar charts
Pie charts
Date histograms
Heatmaps
Data tables
Treemaps
4. Frontend Embedding
Dashboards should be embeddable on a website using iframe or served via Flask and NGINX.
Custom visuals using D3.js or Chart.js if Superset is too limited.
Filters: Dropdown filters for vendor, product, impact, year, etc.
Deliverables:
Working Elasticsearch setup (Docker or Elastic Cloud)
Python scripts for preprocessing and queries
Superset dashboard exports or backups
All source code and deployment instructions (GitHub or GitLab repo)
This project will be hosted internally.
Required Skills:
Python (Pandas, Elasticsearch-py, Flask or FastAPI)
Elasticsearch (data modeling, indexing, querying)
Superset (dashboarding and filters) or Kibana
Optional: D3.js or Chart.js for advanced charts
Docker, NGINX, and reverse proxy configuration
Familiarity with Git
How to Apply:
Share links or screenshots of dashboards or visual data projects you've done before.
Mention your experience with Superset, D3.js, or Elasticsearch.
Describe briefly how you would set this up if it were hosted on a website.