Efficient Data Extraction for Freelancers

We’re building an internal tool to collect and structure publicly available exhibition/exhibitor information from:

Exhibition/trade fair websites (exhibitor directory pages, event pages)
PDF catalogues / exhibitor lists published by organizers
Goal: Convert unstructured information into a clean database/CSV format so it can be searched and analyzed.

What the tool should do

Accept a list of source URLs and/or PDF files
Extract structured fields such as:

Event name, dates, location, organizer
Exhibitor/company name
Exhibitor Profile Description
Booth/stall number (if available)

Category/product tags (if available)

Address/country
Website/email/phone (if publicly visible)

Output to CSV/JSON and optionally to a database (PostgreSQL / MySQL)

Handle pagination, basic duplicates, and consistent formatting
Log failures and provide a simple way to re-run sources

Important compliance requirement

The solution must collect only publicly available information and must respect website terms, robots guidelines where applicable, and rate limits.
No credential bypassing, no CAPTCHA bypassing, no restricted data.

Preferred approach / tech (open to suggestions)

Python (Playwright / Requests + BeautifulSoup) or Node.js (Playwright/Crawlee)

PDF parsing: pdfplumber / tabula / camelot (or equivalent)

Clean code + documentation for setup

Containerized with Docker (nice to have)

Deliverables

Working tool/script (repo or zipped project)

Documentation: setup + run instructions + adding new sources

Sample output files from 3–5 test sources (web + PDF)

Basic tests and error-handling
NO THIRD PARTY PERMISSION, SHOULD WORK INDEPENDENTLY
Provide in your proposal

Similar work examples: data extraction, PDF parsing, automation pipelines

Your recommended architecture

Any limitations you foresee (e.g., sites that block automation)

YOU MUST HAVE PROPERTYME EXPERIENCE. DO NOT APPLY IF YOU DON’T.

I am changing property management systems and moving away from PropertyMe. As there is no easy backup procedure, everything has to be saved manually. I need emails, sms’s, documents, jobs, work orders and inspections saved. There is 60 properties and around 2-3 years of data.

I need this completed by mid - end February. You must be experience to know what you are doing.

Skills: Data Processing, Data Entry, Excel, PDF, Property Management, Data Cleansing, Data Extraction, Database Management, Data Management, Data Backup

We need an experienced data scraper to build a lead list of cleaning companies across North America.

Target Companies:

- Cleaning/janitorial services with 10-200 employees
- Located in USA and Canada
- Active businesses only (no closed/inactive)

Required Data Fields:

- Company name
- Owner/decision maker name
- Business email address
- Phone number
- Physical address
- Estimated company size
- Estimated annual revenue (if available)

Deliverable:

- CSV or Google Sheets
- Minimum 500 verified leads
- Email addresses validated (bounce rate less than 3%)
- Organized by state/city

Requirements:

- Experience scraping Google Maps/Yelp
- Email enrichment tools
- Proven accuracy on previous projects
- Provide sample of 10-20 leads before full payment

Timeline: 5-7 days

Please share:

1. Tools you'll use
2. Sample of similar work
3. Estimated delivery time
4. Cost per lead you'll charge

Hey! I am looking to hire someone to scrape all dry dog food products listed on the PetSmart website. This project includes approximately 800–1,000 unique dry dog food SKUs. For each product, I need the following data captured:
- Product name
- Brand
- Guaranteed analysis (listed out individually by Protein, Fat, Fiber, and Moisture - Flavor Bag size(s)
- Price (s)
- Product image (URL or downloadable image)
- Full ingredient list

All data must be organized exactly according to the structure in the attached Google Sheets. Maintaining this format is critical.

Please confirm: Whether this scope is doable for your team Estimated turnaround time Accuracy and quality control process Any limitations Let me know if you have questions after reviewing the Google Sheet. Thanks!

https://docs.google.com/spreadsheets/d/1v5jDFuwcCpZvGW_eTVH7_8dYfLrVvZQnPiHUleN24_c/edit?usp=sharing

We are seeking a skilled data scraper to extract individual performance ratings for all players across Europe's top 5 football leagues from whoscored.com, dating back to 2012. The ideal candidate should have experience with web scraping techniques and be proficient in handling large datasets. You will be responsible for ensuring data accuracy and consistency. If you have a strong background in data extraction and are detail-oriented, we would love to hear from you!

I am looking for an experienced Python developer to build a personal financial dashboard that consolidates data from 6+ Xero organizations and external spreadsheets into a single "Net Worth" view.

I do not want to pay for monthly subscription software (like Syft/Fathom). I want a custom, lightweight Python application that runs locally on my machine (or is easily deployable) and allows me to visualize my Assets, Liabilities, and Net Equity across all my entities in real-time.

The Stack
Language: Python

Frontend: Streamlit (preferred for speed/simplicity)

Integration: Xero API (OAuth 2.0)

Data Handling: Pandas

Key Deliverables
Authentication Handler:

Implement Xero OAuth 2.0 to securely log in to multiple Xero tenants (organizations) simultaneously.

Crucial: Must handle "Token Refreshing" automatically so I don't have to log in every time I run the script. Tokens should be stored locally/securely.

Data Extraction:

Pull the "Balance Sheet" report for all connected organizations via API.

Extract key line items: Total Assets, Total Liabilities, Net Equity.

External Data Integration:

Read a local CSV/Excel file (e.g., external_assets.csv) containing non-Xero assets (e.g., property values, private equity) and include them in the totals.

The Dashboard (Streamlit):

KPI Cards: Total Net Worth, Total Assets, Total Liabilities.

Charts: Stacked Bar Chart (Assets vs. Liabilities per Entity).

Data Table: A simple view of the raw numbers for verification.

Documentation:

A simple README file explaining how to install dependencies and run the app on my local machine (Mac/Windows).

Required Experience
Must have experience with the Xero API. (Please do not apply if you haven't worked with Xero before; I don't want to pay for research time).

Strong proficiency in Python & Pandas.

Experience building data dashboards with Streamlit or Dash.

Understanding of basic accounting terms (Assets, Liabilities, Equity) is a huge plus.

• Have you successfully connected to the Xero API using OAuth 2.0 before?
• How do you plan to handle the "Token Refresh" logic so I don't get logged out every 30 minutes?
• Have you built a multi-tenant (multi-organization) dashboard before?

We’re looking for a hands-on automation builder who can connect multiple tools (via APIs + webhooks) and embed AI (OpenAI/LLMs) into the workflow to classify, enrich, and respond—without the system breaking every time an edge case shows up.

This is not a “set up a quick Zap” project. We need someone who thinks in systems: clean architecture, strong logging, retries, validation, and documentation so we can maintain and scale it long-term.

What we’re trying to accomplish
We have leads/messages/data coming from multiple places, and we want a reliable pipeline that:

Captures inbound events (forms, emails, chat, etc.)
Normalizes/cleans the data
Uses AI to extract key fields, classify/tag, and optionally draft responses/content
Pushes updates into our tools with proper error handling, retries, and logging
Produces clear documentation + SOPs so our team can operate it
Scope of work (initial phase)
Review our current tools + data flow and propose a simple, scalable workflow architecture
Build automation workflows in n8n and/or Make.com and/or Zapier (whichever best fits)
Implement API integrations + webhooks (including custom logic where needed)
Add AI steps (OpenAI/LLM integration) for:
Classification/tagging
Data extraction + enrichment
AI-assisted customer support responses (drafts, not auto-send unless approved)
Add production-grade reliability:
Error handling, retries, fallbacks
Logging + basic monitoring/alert approach
QA testing with test cases (happy path + edge cases)
Documentation & handover
Setup notes, workflow maps, SOPs, and a short handover walkthrough
Deliverables
A working, tested automation workflow (or set of workflows)
Clear documentation (SOP + “how it works” + “how to troubleshoot”)
A short list of recommended improvements for phase 2 (scaling/optimization)
What we’re looking for
Proven experience with n8n / Make.com / Zapier
Strong experience with API integration and webhooks
Experience embedding AI automation / LLM integration into workflows (OpenAI or similar)
You naturally build with:
Validation, idempotency mindset (avoid duplicates)
Logging, retries, and clean modular design
You communicate clearly and document as you go
Nice to have
Experience building AI agents inside automations (with guardrails)
Experience with scalable data pipelines and structured outputs (JSON, schemas, etc.)
Long-term support/optimization availability after initial build
How to apply
Please include:

Which platform you recommend for this (n8n vs Make.com vs Zapier) and why
1–2 examples of similar projects (AI + automation + multi-app integration)
How you typically handle retries/logging/QA testing
Your availability to start

We are seeking a detail-oriented researcher to extract historical data from Google Maps regarding tenant occupancy in approximately 20 buildings over the past 5 years. The ideal candidate will have experience with data extraction and analysis, as well as familiarity with Google Maps. You will be responsible for compiling and summarizing findings to help us understand tenant turnover trends in these properties. If you are skilled in data research and are able to deliver accurate and timely results, we would love to hear from you!

Skills: Data Entry, Research, Excel, Data Mining, Data Extraction, Data Analysis, Data Collection, Data Management

We are seeking a skilled freelancer to scrape data from hundreds of client PDF scans, ensuring accuracy and completeness. The data will need to be processed, verified, and organized into a specific CSV format for seamless import into our CRM system. Attention to detail and experience with data extraction and manipulation are essential. If you have a proven track record in handling similar projects, we want to hear from you!

• What are your security protocols when it comes to data? The files needed reviewed contain client sensitive data.
• How do you deal with outliers or missing values in a dataset?

About the Project
We need a small, production minded Azure AI POC deployed in our demo environment using real Azure services, not screenshots or sample notebooks. The goal is to create a working example that demonstrates practical Azure AI use cases for small businesses.
You will choose ONE of the following paths:
1. Build POC A (Call Recording Interpreter)
2. Build POC B (RAG Chatbot using customer documents)
3. Propose an alternative Azure AI POC that you believe would be valuable, simple, realistic, and uses Azure AI + Azure compute/storage/search services
We want something that can be:
• built quickly
• deployed cleanly
• turned on/off to control cost
• explained clearly
• used in real demos for SMB customers
If the first engagement is successful, we will hire you again for the second POC or future POCs.
This is a fixed price project.
We do not accept manual time.
________________________________________
POC Options
Option A — Call Recording Interpreter (Preferred Example)
A workflow that ingests audio files and produces structured insights using Azure AI. This may include:
• Audio ingestion via Azure Blob Storage
• Transcription
• Speaker diarization
• Summary
• Sentiment or topic extraction
• Optional: custom JSON fields such as job type, urgency, next steps
• Store results in Blob Storage or Dataverse
Preferred but not required: Azure AI Content Understanding – Audio Analyzer.
If you have a better Azure-native architecture for audio understanding, you are free to propose it.
________________________________________
Option B — RAG Chatbot (Preferred Example)
A lightweight chatbot grounded in business documents using Azure AI. This may include:
• Extracting/normalizing text from PDFs/Word/images
o (Preferred but not required: Azure AI Content Understanding)
• Embeddings + Azure Cognitive Search vector index
• Small backend (Azure Functions or equivalent)
• Azure OpenAI for grounded answers
• Simple UI (web page or Teams tab) for testing
This should look and function like a real Azure-native RAG implementation.
________________________________________
Option C — Your Alternative POC (Fully Welcome)
You may propose a different Azure AI POC that:
• Uses real Azure AI services (OpenAI, Cognitive Search, Content Understanding, Document Intelligence, AI Language, AI Vision, etc.)
• Uses real Azure plumbing (Functions, Storage, Event Grid, App Service, etc.)
• Is small enough to complete in ~10–14 days
• Demonstrates an actionable small business scenario
• Can be turned on/off easily
• Helps our customers understand how Azure AI can improve SMB workflows
Examples could include:
• AI powered email triage
• Document classification/understanding
• AI-assisted small business onboarding
• Structured data extraction workflow
• Lightweight agent or rules assisted automation
You have flexibility.
________________________________________
Knowledge Transfer (Important)
We do not need to maintain or extend the solution on our own.
However, we must fully understand:
• What you built
• Why you built it that way
• How the services interact
• How to run it, test it, and turn it on/off
• How it could be extended in future work
You must be comfortable explaining your architecture and walking us through it.
If the partnership is a good fit, we will continue working with you on the next POC.
________________________________________
This project is fixed price only
(based on clear milestones and deliverables).
________________________________________
Required Skills (Must Have Some of These)
You do not need experience in all items below — but you must be strong in the ones required for your chosen POC.
• Azure AI services (any relevant mix of Content Understanding, OpenAI, Cognitive Search, Document Intelligence, AI Language, AI Vision, etc.)
• Azure Functions (Python or C#)
• Azure Blob Storage
• Clean architecture for small, production style workloads
• Experience building real POCs that use actual Azure resources
• Ability to explain decisions and architecture clearly
________________________________________
Nice to Have
• Dataverse
• Teams apps or simple UIs
• Experience optimizing token usage and request patterns
________________________________________
Deliverables
• Fully working POC deployed in our Azure environment
• Clean, well organized code in our repository
• A short runbook including:
o Architecture
o Resource list + SKUs
o Configurations
o End to end flow
o How to turn things on/off
• A short demo video
• A knowledge transfer call
• A short list of recommended next steps
________________________________________
Timeline
POC should be completed within 10–14 days, including documentation and walkthrough.
________________________________________
How to Apply
Please provide:
1. A brief introduction describing your Azure AI experience
2. Which option you choose: A, B, or C (propose your own)
3. 2–3 examples of Azure AI work you’ve done
4. A short outline of your approach
5. Your fixed price bid and timeline
6. GitHub/portfolio (if available)
________________________________________
Screening Questions (Required)
1. What Azure services would you use for the POC you choose, and why?
2. Provide an example of a real Azure AI POC you built.
3. How do you ensure a POC uses actual Azure resources and not mockups?
4. How do you design Azure solutions that can be easily turned off when not in use?
5. How do you help clients understand a solution without expecting them to maintain it?
________________________________________
Final Note
We are looking for someone who can:
• Deliver efficiently
• Build clean, realistic Azure AI solutions
• Communicate clearly
• Demonstrate real Azure expertise

*SEE ATTACHED SCOPE OF WORK FILE

Build a complete Google Sheets product catalog for the Jackets and Gilets category on the Pas Normal Studios website.
This is a data extraction and structuring task.

Source
All information comes from the Pas Normal Studios Jackets & Gilets collection and individual product detail pages. You should complete an entry for every item on this category page.

• https://pasnormalstudios.com/us/collections/jackets-gilets

Deliverable
– One Google Sheets file that exactly matches the attached CSV example row in:
– Column names
– Column order
– Data types
– Formatting (comma-separated values where required, image folder links)

You will be given an example CSV. You must copy its structure perfectly and fill every field.

Scope of Work

1. Product Coverage
– Include every jacket and gilet currently listed in the Jackets & Gilets collection.

2. CSV / Sheet Requirements
– Use the provided CSV as the correct schema
– Do not rename, reorder, add, or remove columns
– Fill every field
– Follow the same formatting rules used in the example (including comma-separated lists in some fields

Some fields are direct copy from the PDP. Others require assembling values across variants (for example, available colors).

3. Images
You must collect and link images at two levels:
A: Item-level images (shared across all variants of a product):
– Lifestyle images
– Detail images
– Any non-color-specific imagery shown on the PDP
These images are reused across all color variants of the same product.

B: Variant-level images (color-specific):
– Product images for each color
– Each color variant must have its own set of images
– These must be separated from item-level images in the correct CSV fields

All image URLs must be direct, stable links in the supplied google folder here: https://drive.google.com/drive/folders/1ggp2zczGuQi3eivgZl58Wkl-dnF9PuSu?usp=drive_link
Follow the same organization and structure as the example entry.


4. Variants
For each product:
A: Capture all available color variants
B: Populate color fields as comma-separated values where required
C: Ensure variant data aligns correctly with shared item-level data


5. Sizing Charts
A: Locate the sizing chart on the PDP for each product
B: Take a clear screenshot of the sizing chart
C: Provide one sizing chart per product (shared across all variants)
D: Link or reference the sizing chart consistently per the CSV example


6. Pricing and Status
A: Capture current pricing (including sale prices where applicable)
B: Capture availability or status if represented in the example CSV
C: Follow the exact formatting shown in the example


Job Priority
FIRST: Organize the CSV first
SECOND: Capture and link images second


Quality:
– No formatting deviations
– All URLs must work
– Comma-separated fields must be clean and consistent
– The sheet should be ready for direct ingestion into a catalog system without cleanup


What I Provide
– Example CSV file that defines the exact schema
– Link to the Pas Normal Studios Jackets & Gilets collection
–  Sample folder for use in storing the image assets

What You Provide
– Completed Google Sheets file matching the CSV example exactly
– All required images and/or video and sizing chart screenshots correctly linked
– A short note confirming completion and any edge cases found (for example, missing data on PDPs)

Ideal Freelancer
– Expert in data extraction
– Extremely detail-oriented
– Comfortable working with large product catalogs
– Experienced with ecommerce data, variants, and image structuring
– Confident extracting structured data from PDPs and Shopify-style sites


DEMO CSV: https://docs.google.com/spreadsheets/d/1TBQgqzgXx6aLoOeE_erFK-0D2AAxvm1gdGvGmpRsxww/edit?usp=sharing

Project Overview:
We are looking for an experienced Data Analyst and Business Intelligence (BI) Specialist to lead a unique analytical project for a major high-density tourism city.
​The objective is not to produce a traditional economic report, but to generate a "Missed Opportunities & Market Gaps Report." The goal is to identify "Unmet Demand" by analyzing big data, digital footprints, and user behavior to pinpoint exactly what visitors and residents are looking for but cannot find.
​Scope of Work:
​1. Data Scraping & Geospatial Analysis:
​Google Maps Analysis: Extract data for thousands of Points of Interest (POIs) in the target zone (Hospitality, F&B, Services). Conduct a "Supply Gap Analysis" to identify neighborhoods that lack specific services relative to their population density/footfall.
​Occupancy & Pricing Trends: Scrape historical data from OTA platforms (e.g., Booking.com, Agoda) to identify "Sold Out" periods and price surges. The goal is to build a "Room Shortage Index."
​2. NLP & Sentiment Analysis (Social Listening):
​Social Data Mining: Aggregate and analyze thousands of reviews and posts from platforms like X (Twitter), TikTok, and Google Reviews related to the specific region.
​Complaint Categorization (Voice of Customer): Filter negative sentiment to detect specific "Unmet Needs" (e.g., specific complaints about a lack of transportation, missing cuisine types, unavailability of family facilities).
​3. Reporting & Visualization:
​Data cleaning and preprocessing.
​Developing an Interactive Dashboard featuring Heatmaps to visualize investment gaps.
​Required Tech Stack:
The freelancer must be proficient in the following tools (or equivalent):
​Data Extraction: Expert use of tools like Outscraper (for Google Maps), Apify, or Python libraries (BeautifulSoup/Selenium/Scrapy).
​Geospatial Analysis: Ability to handle GIS data for spatial visualization.
​NLP: Experience in Natural Language Processing (specifically for Arabic text analysis is a major plus).
​Visualization: Advanced proficiency in Power BI or Tableau.
​Deliverables:
​Cleaned Datasets: (Excel/CSV) containing all scraped and structured data.
​The "Market Gaps" Report: A PDF summary highlighting the Top 10 Missed Investment Opportunities based on the data.
​Source Dashboard File: A fully functional Power BI/Tableau file containing:
​Unmet Demand Indicators.
​Service Gap Heatmaps.
​Word Clouds for recurring user requests/complaints.
​Requirements:
​Please attach a Portfolio of similar past projects (specifically involving Web Scraping + Geospatial/Market Analysis).
​Ability to process and analyze data in Arabic (the source content is in Arabic).
​Strict adherence to data confidentiality regarding the target location.
​Screening Questions for Applicants:
​What methodology would you use to estimate "Unmet Demand" in a specific neighborhood where official data is scarce?
​How do you handle spam or irrelevant bots when analyzing social media sentiment to ensure accuracy?
​Have you used Outscraper or the Google Places API in previous projects? Please describe briefly.

Web Scraping Job Post

We are looking for a proficient web scraper for the purpose to help create lead lists. This project will consist of scraping 5 websites that have physicians, nurse practitioners and physician assistants of all medical specialties. Feel free to use python or whatever resources you have to pull the following information: First Name, Last Name, Profession(physician, NP, PA), Specialty(Family Med, Internal Med, etc.), State, E-mail and Phone Number; if you can find all these items. We do realize that some of the emails and phone numbers may not be listed on the websites.

We are looking to start the project this week and finish the project by next weekend. There will be a completion bonus if you finish the project and the bonus will increase if the project is finished early. 



UPMC
https://www.upmc.com

Jefferson Health
https://www.jeffersonhealth.org

Penn Medicine
https://www.pennmedicine.org

Allegheny Health Network (AHN)
https://www.ahn.org

Geisinger
https://www.geisinger.org

I am seeking a freelancer to build a full automatic data extraction and enrichment pipeline for Spanish procurement PDF documents.

Scope of Work:

Phase 1 – PDF Extraction
Extract product names and key features from Spanish PDF files.
Clean and structure the data.
Output results in Excel (.xlsx) format.

Phase 2 – Web Search & Price Extraction
Use each product name as a Google search query, restricted to a specific domain.
Analyze the top 5 search results per product.
From each URL, extract with high precision:
Price (must extract 5 accurate prices per product)
Brand
Product features and description
Identify similar products, not only exact matches.
Measure similarity using methods such as Levenshtein distance or cosine similarity.

Deliverables
Final datasets in Excel (.xlsx) and JSON (.json) formats. (Do not forget price extraction must be very precise and sufficient. We need 5 successful URL scraping)
A detailed README.md explaining the full workflow, tools, and how to reproduce the process.

Technical Notes
Use of LLMs for extraction, similarity analysis, and enrichment is highly recommended.
The solution must be accurate, efficient, and fully reproducible.

Skills: Python, Data Processing, Excel, Web Scraping, JSON, Data Extraction, Data Analysis, API Integration, Google Search, Natural Language Processing

I'm looking for a skilled developer to create AI software that scrapes academic websites for due dates and integrates them into popular calendar systems.

Requirements:
- Scrape assignment due dates, exam schedules, project deadlines, and any other date-related tasks
- Integrate with Google Calendar, Outlook Calendar, and Apple Calendar

Ideal Skills:
- Experience with web scraping technologies
- Familiarity with AI and data extraction
- Strong integration skills with major calendar platforms

Looking for a reliable and efficient solution to help manage academic deadlines. Please share your relevant experience.

Skills: PHP, Java, Web Scraping, Software Architecture, Data Extraction, API Integration, AI Model Development, AI Development

I run a systematic Saudi “Sector + Stock Selector” framework for Tadawul equities and SAR macro risk posture. My holding horizon is 3–6 months, but I want to run the analysis weekly to monitor changes, identify red flags early, and adjust conviction/sizing or exit triggers when evidence shifts.
The bottleneck is consistent, auditable data ingestion from Saudi primary sources (central bank/statistics/fiscal/market) plus key market and global overlay series. I want you to build a pipeline so that each week it produces a complete “Run Pack” in a single Google Drive folder, and then Manus can read that pack and generate the report without me uploading ZIP files into chat.

Please refer to the attached scope of work for details.

• Describe your recent experience with similar projects
• What frameworks have you worked with?
• Describe your approach to testing and improving QA

I require assistance with exporting specific chat logs from my Discord account. These messages contain critical information, and it is essential that they are preserved for long-term storage.

Previous attempts to extract this data have failed, so I am seeking an expert who can handle this efficiently. Please apply if you have successfully completed similar Discord data backup projects in the past.

Key Requirements:
Proven experience with Discord data extraction or specialized export tools.
Ability to deliver the logs in a clean, readable format (e.g., PDF, HTML, or CSV).
Strong commitment to data privacy and confidentiality.

We’re looking for an experienced Excel expert to collaborate with our Product and Sales teams to reorganize, standardize, and program our current Excel-based pricing tool (“pricer”). This pricer is used by Sales to quickly and accurately price our professional services and SaaS offering, generate consistent outputs, and reduce manual errors.
The right candidate is equal parts Excel architect + business analyst: you can translate pricing rules into a clean, maintainable model, and you can work directly with stakeholders to clarify assumptions, edge cases, and required outputs.
What You’ll Do
Assess and map the current pricer
Review our existing Excel file(s), logic, tabs, formulas, and outputs
Identify pain points: manual steps, inconsistencies, broken formulas, version confusion, and error-prone inputs
Reorganize the workbook into a clean structure
Separate clearly: Inputs, Assumptions, Rate cards, Calculations, Outputs, Admin/Config
Create a “guided” input experience (validated fields, clear instructions, and guardrails)
Program the pricer logic
Implement pricing rules for both professional services and SaaS (rate cards, tiers, minimums, bundles, discounts, term lengths, one-time vs recurring, ramp/phase pricing, etc.)
Add checks and controls: error flags, required fields, sanity checks, and auditability
Ensure results are consistent and reproducible regardless of user
Standardize outputs used by Sales
Generate clean, client-ready summary outputs (pricing summary, line items, monthly/annual totals, term totals, implementation fees, etc.)
Optional: export-friendly formatting that can drop into proposals/quotes
Improve usability and maintainability
Build for long-term maintenance (clear naming, documentation, versioning, minimal “magic” formulas)
Create a simple admin section so we can update rate cards/assumptions without breaking the model
Collaboration and iteration
Work in short cycles with Sales + Product to validate logic and iterate quickly
Capture edge cases and document how the model handles them
Deliverables
A rebuilt Excel pricer that is:
Easy to use (guided inputs, minimal manual steps)
Accurate (pricing rules implemented correctly)
Maintainable (clean structure + documentation)
Documentation package:
“How to use” guide for Sales
“How to maintain” guide for Product/Admin (rate updates, rule changes, adding new SKUs, etc.)
Optional enhancements (if scoped):
Scenario comparison (Option A vs B)
Quote summary PDF-ready tab
Basic VBA or Office Scripts automation (only if needed)
Required Skills
Advanced Excel: complex formulas, structured references, data validation, error handling, named ranges
Strong workbook architecture and organization skills
Ability to translate business rules into a reliable model
Comfortable collaborating with non-technical stakeholders (Sales/Product)
High attention to detail and testing mindset
Nice to Have
Experience with CPQ-style logic (configure/price/quote concepts)
Power Query, pivot tables, Excel “model” best practices
VBA / Office Scripts (only if it meaningfully improves workflow)
Prior work building pricing calculators for services + SaaS
What Success Looks Like
Sales can price deals faster with fewer mistakes
The model is consistent across users and use-cases
We can update rate cards/assumptions without rewriting formulas
Stakeholders trust the outputs enough to use them in proposals/quotes
Project Type / Timeline
Start with a short discovery phase (review current file + requirements)
Then build and iterate in milestones
We’re open to hourly or fixed-price with clear deliverables
To Apply (Please Include)
A brief summary of similar Excel pricing / calculator work you’ve done
Your proposed approach (how you’ll structure the workbook + validate logic)
Any examples/screenshots (redacted) of prior models you’ve built
Your availability and estimated turnaround for a first working prototype