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Structured and modeled subcontractor bid data to evaluate cost, scope coverage, and readiness across vendors for a $25 million dollar construction project.
Built a relational model, cleaned data with Python Pandas, and DAX measures to calculate total cost, coverage %, readiness %, and pending scope items, enabling comparative analysis between subcontractors.
Delivered a Power BI dashboard to support bid evaluation, vendor selection, and decision-making based on cost efficiency and scope completeness.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
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This project demonstrates my data entry and web research skills. I created a structured spreadsheet containing professional contacts including names, job positions, companies, emails, and phone numbers. The data is organized clearly for business outreach and lead generation purposes.
Clean structure makes outreach datasets far more usable.
When fields like role, company, and contact info are standardized, it becomes much easier to filter, segment, and build targeted outreach lists.
Heard about Bright Data for the first time today and decided to check it out.
Looked at their n8n integration and... I have questions. 😅
For those who don't know...it's basically infrastructure for pulling data from any website at scale. Proxies, scrapers, ready-made datasets.
As someone who builds automations, I can already see where this fits.
Still wrapping my head around it though.
Has anyone used Bright Data in their workflows? What did you build with it?
Interesting workflow. Combining n8n with Bright Data for automated data collection seems really powerful.
Concept product exploring SaaS analytics and executive performance dashboards.
Just finished designing SignalBoard, a SaaS analytics dashboard built to help teams understand growth metrics at a glance.
Many SaaS companies track revenue, customer growth, retention, and acquisition across multiple tools. The goal of this concept was to centralize those insights into a single analytics workspace.
The interface includes dashboards for revenue performance, customer segmentation, retention cohorts, and acquisition funnels so teams can quickly identify trends and make informed decisions.
Design focus:
• Data dense interfaces that remain readable
• Clear visualization of complex SaaS metrics
• Executive level performance monitoring
Focusing more on B2B SaaS analytics and dashboard design lately.
Really clean way of handling dense data. I love how each metric feels digestible without losing context executive dashboards often fail at readability, but this feels balanced.
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