Startup Funding Data Collection in Python

@ZohaibRoy

⭐⭐⭐⭐⭐ Web Scraping for Recently Funded Companies in USA, Europe & Australia ❇️ Hi My Friend, I hope you are doing well. I checked all of your project requirements and I can see you are looking for a Web Scraping Developer. You have no need to look any further as Zohaib is here to help you! My team is already doing 50+ similar projects for startup funding data. I will build a robust Scrapy + Selenium workflow that gathers the required fields, cleans duplicates and saves everything to a tidy CSV. I will also add a simple Python script for monthly updates at no extra cost. ➡️ Why Me? I can easily do your data collection as I have 5 years of experience in Python, Scrapy, Selenium, BeautifulSoup, Pandas, API integration. Not only this, I have a strong grip on data cleaning, Excel automation and web automation. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing with you in chat. ➡️ Skills & Experience: ✅ Python ✅ Scrapy ✅ Selenium ✅ Playwright ✅ BeautifulSoup ✅ Pandas ✅ Data Cleaning ✅ API Integration ✅ Excel Export ✅ CSV Handling ✅ Duplicate Removal ✅ Documentation Waiting for your response! Best Regards, Zohaib

@intelliresponse

With more than a decade of experience in python web automation, data mining and extraction, I am confident I can build you the structured, updatable database of recently funded companies you need. My forte lies in `Python` and `C-based` projects, earmarking me as the specialist to execute your project focusing on high-impact, result-driven solutions. Being highly-specialised in web automation, scrapping AI & Python solutions, I can deliver an automated workflow for your future updates if needed. Detailed documentation outlining the entire data collection process will be provided too along with clear reporting of coverage, limitations and any unavailable fields.

@mubeenm5026

With over a decade of experience in web and software development, including proficiency in Python, I believe I'm the best fit for your **Startup Funding Data Collection** project. At Web Crest, we are well-versed in a variety of tools, including BeautifulSoup, Scrapy and Selenium, all fitted to handle the complex nature of web scraping. We have previously designed scraping scripts for various clients and can guarantee that our work will be reusable and future-proof. Overseeing your project would be a continuation of our commitment to providing scalable solutions for businesses. Informed by APIs integration and experienced data cleaning through Pandas, we are confident in assuring you that our resultant dataset will not only be meticulously curated but also easily verifiable. Delivering a clean Excel/CSV database coupled with an automation workflow is our specialty; such should include any necessary documentation required for your executable transparency.

@TeraByte26

Hi Shyam, I will deliver a deduplicated CSV with the 16 fields you Waiting for your response in chat! Best Regards.

@ganeshrasekar

With over 5 years' experience as a developer, much of it focused on web scraping, data extraction, and Python, I am confident in my ability to tackle the Startup Funding Data Collection project. My skillset includes proficiency in BeautifulSoup, Scrapy, Selenium, and using API integrations to streamline data collection. I am also no stranger to handling structured and semi-structured data, which will prove invaluable when organizing the extensive amount of information associated with company funding. I believe my past projects reflect my ability to deliver reliable, well-structured results within agreed timelines and budgets. In fact, I have previously built similar Excel/CSV databases for investors and entrepreneurs alike - encompassing data points such as company details, funding rounds, amount raised, sources of funding and more. Thus maximizing my efficiency by incorporating reusable scraping scripts or automated workflows will be second nature to me.

@prashanpatel557

Hello, I can build a reliable Python-based startup funding data collection workflow covering the USA, Europe and Australia, with clean, deduplicated and verifiable output. I have experience with Python web scraping, BeautifulSoup/Scrapy, Playwright, APIs, Pandas, data cleaning and reusable automation workflows. I can deliver: - Recently funded company discovery from multiple public sources - Company, website, LinkedIn, location and industry data - Funding round, amount, date and total funding - Investors and company description - Founder/CEO information where publicly available - Source URL for every record - Deduplication and data normalisation - Excel/CSV database with consistent fields - Reusable Python scraper/automation for future updates - Logging, validation and failed-record reporting - Documentation covering sources, methodology and limitations I would use a multi-source approach rather than relying on a single database, with the agreed 30–90 day funding window and source verification built into the workflow. Estimated delivery: 7–10 days Budget: $400–550 I can also structure the system for recurring weekly/monthly updates.

@haciyevqurban

As a highly skilled and experienced Python developer, I am confident in my ability to undertake your Startup Funding Data Collection project with utmost efficiency and accuracy. Over the past 4+ years, I have honed my expertise in a range of technologies and frameworks including BeautifulSoup, Scrapy, and Selenium - all essential in ensuring a robust web scraping workflow like what your project requires. Moreover, my proficiency in handling structured and semi-structured data, coupled with my knowledge of employing various APIs for data integration will significantly add value to your project. By utilizing my mastery of Pandas, I will ensure the cleanliness and accuracy of all extracted data. Furthermore, I offer not just advanced technical expertise but also strong work ethics. I pride myself on maintaining strict adherence to website terms, access restrictions, and privacy requirements when conducting any web scraping activities. Let me build precise and reliable data for you without unnecessary duplicates, considering specifically recent funding announcements for the last 30-90 days.

@Sv3nie

Hi, I can have this done for you very fast, check my profiles and reviews. And lets get in contact first.

@Yevhen1987

Hello. I specialize in web scraping with Python and can effectively collect the startup funding data you need from USA, Europe, and Australia. I will use Scrapy/Playwright for efficient data extraction from various public sources, combined with Pandas for comprehensive cleaning, deduplication, and structuring into a clean Excel/CSV database. My process will include identifying reliable data sources for recent funding rounds (last 30-90 days), extracting all 16 specified data fields, and delivering a reusable Python script along with documentation. For an initial dataset of up to 500-700 records, I can complete this for $450 within 3-5 days. To start, what are your primary target sources or preferred initial data volume for a test run? Best regards, Yevhen.

@dataspro

Nice to meet you , It is a pleasure to communicate with you. My name is Anthony Muñoz, I am the lead engineer for DSPro IT agency and I would like to offer you my professional services. I have more than 10 years of working as a Backend and Software developer, I have successfully completed numerous jobs similar to yours therefore, and after carefully reading the requirements of your project, I consider this job to be suitable to my area of knowledge and skills. I would love to work together to make this project a reality. I greatly appreciate the time provided and I remain pending for any questions or comments. Feel free to contact me. Greetings

@ambientinfotech

Hi there, Writing working Python code is easy; building a fast, scalable, and memory-efficient system that doesn't break under load is where most implementations fail. As a Senior Python Engineer, I engineer robust backend architectures, automation pipelines, and custom API systems built for long-term production use: • Modern Backend APIs: High-performance, asynchronous REST & GraphQL services using FastAPI, Django, or Flask. • Intelligent Automation & Scraping: Headless browser automation (Playwright/Selenium), robust data scrapers with rotating proxy/anti-bot bypass, and custom Celery/Redis worker queues. • AI & LLM Integrations: Seamless integration with OpenAI, LangChain, vector databases (Pinecone/Chroma), and automated data pipelines. • Database & Query Optimization: PostgreSQL, MongoDB, Redis caching, and Dockerized deployments designed for high throughput and zero bottlenecks. I write clean, PEP-8 compliant, fully documented code that your team can easily maintain and scale. Do you have a technical spec, repo, or API documentation ready? Drop me a message in chat, and let’s discuss the fastest way to execute this. Best regards, Deva | Senior Python & Backend Engineer

@Joya1996

A funding database only becomes useful if the records are recent, deduplicated, and traceable back to the original announcement. I’d treat this as a data-quality project as much as a scraping one, using Python to collect, standardize, merge, and verify company records across multiple public sources. My experience is strongest in pulling structured and semi-structured data with Python, Pandas, BeautifulSoup, and browser automation where pages need rendering. For a startup funding dataset like this, I’d work from funding/news sources, public startup listings, and company-level pages to capture company details, round data, investors, and source URLs while clearly marking unavailable fields instead of forcing bad data. If helpful, I can also include a short process note so recurring collection is easier later. - Do you already have preferred sources you want included or excluded? - Approximately how many companies would make this first phase successful for you? - Should founder/CEO LinkedIn collection be limited strictly to clearly public profiles?

@bohdan38

Hello, The main challenge is building a reliable, deduplicated database of recently funded companies across the USA, Europe, and Australia, while preserving verifiable sources and clearly reporting unavailable fields. I can deliver an initial milestone focused on the last 30-90 days, with an agreed target of approximately 150-250 qualified company records. I would use Python with Requests, BeautifulSoup, Scrapy or Playwright where permitted, and Pandas for normalization, validation, deduplication, and Excel or CSV export. Potential sources include public funding announcements, company newsrooms, reputable startup databases, venture capital portfolio pages, and business news sites, while respecting terms, access restrictions, and privacy requirements. Similar work includes structured web data collection, semi-structured content extraction, API integrations, entity matching, and reusable automation workflows. Each record will include source URLs and consistent funding, location, sector, investor, and leadership fields wherever publicly available. Within 7 days, I will provide the first working dataset, scraping workflow, documentation, and a coverage and limitations report. The implementation will be clean, maintainable, and ready for recurring updates. Best regards, Bohdan

@SoftSandila

A web scraping project to collect and organize data on recently funded companies across the USA, Europe and Australia is exactly the kind of data collection work I do. Your brief is clear: build a structured, accurate and regularly updatable database of companies that have recently raised funding, including company details, funding information, website and key decision-makers where publicly available. I would build this as a Python scraping pipeline that identifies recently funded companies from public sources, extracts the relevant data points and organizes them into a clean structured database. On the approach: I would first identify the best public sources for funding announcements across the three regions, then build scrapers that extract company name, funding amount, funding round, date, website and any available contact information. The data would be deduplicated, validated and stored in a format that is easy to query and update. The deliverable is a structured database you can use for outreach or research. A few questions: 1. Do you have a preferred format for the output - CSV, Excel, or a database you can query? 2. How frequently do you need the data updated, and is this a one-time collection or an ongoing subscription? 3. Are there specific data points beyond what you listed that are most valuable for your use case? Happy to share recent web scraping or data collection work on request.

@GobindSawarn

i have build a script with the help of that recently i have scrapped 200k properties listing data for a uk based client i hope i can help you in that also

@NEHABHAT92

With almost a decade in web development and mobile application, I've honed my skills in Python to an exceedingly high level. My expertise, which include mining and organizing data from various sources, aligns seamlessly with the demands of your project. I am well-versed in BeautifulSoup, Scrapy, Selenium/Playwright, Pandas, and API integration - all crucial to extracting the accurate and structured data you require. To ensure your complete satisfaction, I'm committed to delivering clean and deduplicated Excel database files along with source URLs for validation purposes. I have ample experience scraping websites while toeing the line on terms of use, scraping restrictions, and maintaining privacy - all important elements for your project that I can handle without trouble. What differentiates me is that I offer not just technical excellence but also an unbeatable sense of responsibility and reliability. You are assured not just superior quality data but also seamless after-service support. Let's make your project a reality!

@massihm

Hello, Collecting startup funding data across multiple regions often hits snags with inconsistent website structures and anti-scraping measures. I’ll use Playwright for dynamic content, paired with Scrapy for efficient crawling, ensuring we capture all 16 fields reliably. The data will be cleaned and deduplicated using Pandas, delivered in a structured CSV within 3 days. Can you confirm if you have preferred sources for funding announcements, or should I focus on Crunchbase and AngelList as primary targets?

@rakhshanz3

As an AI and ML expert with nearly two decades of experience, I can assure you that no other candidate can deliver the level of technical depth and industry insight that I can. My proficiency in Python and web scraping, particularly using BeautifulSoup and Scrapy, is unparalleled. Furthermore, my adeptness in Selenium or Playwright guarantees seamless extraction from websites burdened with restrictions. . My commitment to excellence does not end with just delivering a database; I believe in empowering my clients. So, along with the completed project, I will furnish you with an easy-to-understand document that details the entirety of the data collection process. For future updates, if necessary, I am more than capable of developing a reusable scraping script or efficient automated workflow. Given my robust technical background, astute appreciation for data accuracy and thoughtful consideration of your project's nuanced demands, there is simply no one better suited to champion the success of your startup funding data collection project than me!

@xiaoven

I have strong experience in Python web scraping, data mining, and data processing. I can collect startup funding data across USA, Europe, and Australia using BeautifulSoup, Selenium, API integration, and Pandas. I will extract all requested fields, deduplicate records, and deliver clean CSV/Excel with source URLs. I will also provide a reusable scraping script. I can deliver within 5 days.