Data Center Contacts Compilation

@imraz2016

I am experienced with Python Projects. Hi There, I have strong experience with Python-based lead research, data-center market research, LinkedIn/Apollo sourcing, construction-project research, and verified B2B contact enrichment. I can build the two separate lists exactly as requested: **Data Centers Under Construction** as first priority and **Planned Data Centers** as second priority. I’ll focus specifically on decision-makers involved in fiber, OSP, telecom, network infrastructure, construction, engineering, procurement, and RCDD/design work connected to active U.S. data-center projects. Each contact will be researched against the actual project/company, and I’ll include the additional **“Why was this contact selected?”** column explaining the relevance rather than supplying a generic contact list. I’m ready to start with a sample batch for accuracy review.

@OutsourceMan

Hello, I am experienced with Python Projects. CnEL India can handle this data-center contact research with a strong focus on **recent, relevant and project-specific contacts**, rather than outdated lists. Our methodology: • Identify USA data centers currently **under construction** and **planned**, using recent project/construction information. • Research the relevant decision-makers across construction, OSP/telecom, fiber, network infrastructure, engineering, procurement and project management. • Use Python-assisted research and structured data collection to organize the information efficiently. • Verify company, role, project relevance and available contact details before adding records. • Separate the final database into **Under Construction** and **Planned** lists according to your priority. • Include company, address, website, phone, LinkedIn, Facebook and other requested fields. • Add a dedicated **“Why was this contact selected?”** column explaining the contact's relevance to the specific project. • Perform a final quality check to remove duplicates, outdated/relevantity issues and incomplete records. We understand that your priority is **excellent recent contacts connected to active projects**, so our research will focus on relevance and freshness rather than simply maximizing the number of contacts. Best regards, CnEL India

@afua23

Hello there, "I am experienced with Python Projects" I have read your project with title Data Center Contacts Compilation carefully and i can help you. I have a 15+ experience in web scraping. Here are a few examples of past projects i have completed here: https://www.freelancer.com/projects/data-entry/Scrape-Data-from-Websites-39311571/details https://www.freelancer.com/projects/data-extraction/Web-Database-Extraction-Script/details https://www.freelancer.com/projects/php/PYTHON-SCRAPER-35879443/details https://www.freelancer.com/projects/excel/Web-scraping-for-public-information/details Let's chat so that we can discuss the fine details/ Thanks Claudia

@ShaikhAneesa

Hi there, I have analyzed the project requirements for the Data Center Contacts Compilation project, specifically focusing on compiling contacts in the data center workforce related to fiber cable users in the USA. Let's chat and discuss it further. To handle your project, I will start with utilizing Python for data extraction and manipulation. My approach involves leveraging web scraping tools and APIs to gather recent and accurate contact information of individuals working on active projects in data centers under construction and planned. I will ensure the Excel sheet includes all necessary details such as company, address, URL, phone, LinkedIn, and a column for the selection rationale of each contact. Clear deliverables for this project include a comprehensive Excel sheet with categorized contacts based on priority lists and additional information column. Before signing-off my bid, I would like to ask a question, i.e., "Have you identified specific data center locations or regions in the USA for this contact compilation?" Warm Regards, Aneesa.

@justmian876

As someone deeply committed to delivering comprehensive data solutions, I believe I am the ideal candidate for your project. My Python skills combined with our firm's specialization in web scraping and large-scale data extraction distinguishes me from other candidates. With my team's proven expertise of processing over 1 million+ data records daily, we not only guarantee fast and efficient results but also uncompromising accuracy.

@sajjadtaghvaeifr

I am experienced with Python Projects. Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@TanvirKhanPaple

I am experienced with Python Projects. Hi there! Project is very clear to me and I can research recent, verified data center contacts in the USA, separating under-construction and planned projects. I can verify roles, project activity, contact details, and provide the required Excel sheet Just message me I am ready to start now and i will show you few data sample before start. Thank you.

@brashel

I am experienced with Python Projects. Hello, This is a strong fit for my Python-based web research, lead generation, and data enrichment experience. I can build two separate USA data-center contact lists focused specifically on active under-construction and planned projects, prioritising the roles and fiber/OSP/network decision-makers you provided. I’ll research recent project information, identify relevant companies and decision-makers, verify contact details, and include company, address, website, phone, LinkedIn/Facebook where available, plus the important “Why was this contact selected?” column with a clear justification based on the project and role. I’ll prioritise fresh, active-project contacts rather than relying on outdated databases, and keep the Excel file clean, deduplicated, and easy to filter by project status and contact role. Ready to begin with a sample batch so you can verify the research quality and freshness.

@gotucat

Hi, I reviewed the task to compile excellent recent data center workforce contacts in the USA, focusing on active-project teams for under construction and planned sites. I’ll build a Python-driven workflow to research companies and roles for fiber cable users and OSP/telecom project activity, then structure results into the Excel columns you specified. I’ll collect and verify company, address, website, phone, LinkedIn, and Facebook, plus add the extra “Why was this contact selected?” column. I’ll keep the sheet clean, consistent, and easy to manage with accurate Data Management from source discovery to final output. Let’s discuss here now.

@DeveloperStation

Hi, I am experienced with Python Projects. The tricky part on a list like this is not scraping names, it's keeping it recent and filtered to people actually tied to active builds. I'd work from real signals: permits and construction filings, recent LinkedIn role activity, press on planned and under-construction sites, then cross check each contact so the "Why selected" column has a real reason, not a guess. We build automation and scraping workflows in Python, including an internal AI-driven business development tool that pulls, enriches, and verifies contact data before it hits a sheet. Two lists as you laid out, with the columns you named plus source and date verified. One question: any specific US regions to prioritize first, or nationwide? Adil

@glanegonsalves

I am experienced with Python Projects. Hi, Glane here. I can build the USA data-center contact dataset using Python based research and data processing, focusing specifically on recent, active projects rather than outdated contact lists. I’ll create two separate Excel lists for (1) data centers under construction and (2) planned data centers, targeting the exact roles you specified across data-center construction, OSP/telecom, network infrastructure, fiber installation, engineering/design, procurement, utilities, and project management. For every contact, I’ll include company, contact name/title, location/address, website, phone, professional/social links where publicly available, project status, and a dedicated Why was this contact selected? column explaining the relevance to your high-count fiber/armored OSP cable market. I’ll also apply quality checks to minimize outdated, duplicate, irrelevant, or unverifiable contacts and deliver the research in a clean, professionally formatted Excel workbook.

@ImranAli01

Hi, I got that you are looking for recent and excellent contacts in the data center workforce, specifically focusing on "FIBER CABLE USERS" and individuals involved in active projects for data centers under construction and planned. This is what I can help you with, let's chat. My approach is to utilize advanced web scraping techniques using Python to compile a comprehensive list of contacts in the specified roles within the data center industry. By leveraging Python's data extraction capabilities, I will ensure the accuracy and freshness of the contact information gathered. The outcome will be a meticulously organized Excel sheet containing all the required details, including company information, addresses, URLs, phone numbers, and social media profiles. This data will be instrumental in connecting you with the key decision-makers and professionals in the field. As final deliverables, you will receive a detailed Excel spreadsheet with the requested contact information, categorized based on the priority lists provided. Each entry will include a justification for why that particular contact was selected, ensuring transparency and relevance in the compilation process. One thing I'd like to confirm before we start: Are there any specific criteria or additional details you would like to include in the contact selection process? Let's discuss further and ensure a tailored approach to meet your requirements. Kind Regards, Imran Ali

@mattafareed4848

Hello I am experienced with Python Projects. I can research excellent, recent contacts for active U.S. data center projects, separating under-construction and planned facilities, and provide a clean Excel list with company details, contact information, LinkedIn, URLs, and a clear “Why was this contact selected?” column. My fee is $___ for the project. Regards Muhammad

@mayankinnovative

Hi, As per my understanding: I am experienced with Python Projects. The real problem isn't finding job titles, it's that "under construction" and "planned" are moving targets, a stale directory is worthless here, so each contact needs to trace back to a recent, verifiable source, which is exactly what your "why selected" column is meant to prove. Implementation approach: I'd build this as a research pipeline rather than a single scrape, pulling from recent industry sources, Data Center Dynamics, construction permits, project trackers, to identify which data centers are actually active or planned right now, then cross-referencing each project's company against the specified titles to find the actual people. Every contact gets logged with the concrete evidence behind it, project name, source, date, so the justification column is real, not filler. Output goes into Excel as two separate tabs for the priority lists, with company, address, URL, phone, LinkedIn, and Facebook filled per contact. A few quick questions: 1. Any specific states or regions to prioritize, or nationwide? 2. Do you have LinkedIn Sales Navigator access I should use for sourcing? 3. Roughly how many verified contacts per list are you expecting?

@TeraByte26

Hi Tim, I am experienced with Python Projects. I will deliver two Excel sheets of recent US data‑center contacts—under‑construction and planned—each with company, address, Waiting for your response in chat! Best Regards.

@carlosd178

Hello, I am experienced with Python Projects. As a result of a detailed review of your requirements, I understand that the priority is not building a generic contact list, but identifying recent, relevant people connected to active U.S. data-center projects—separating facilities currently under construction from those still planned. I have experience with Python-based data collection, data mining, validation, deduplication, and structured Excel delivery, and I'm available to start right away. For this project, I would build two separate datasets, verify project status and company relevance, then target the specific construction, telecom, fiber, engineering, infrastructure, procurement, and RCDD roles you listed. Each record can include company, location, website, phone, LinkedIn/Facebook where publicly available, project status, role, and your required “Why was this contact selected?” column. I would also prioritize recent sources and flag records that cannot be confidently verified rather than filling the sheet with weak or outdated contacts. I have two quick questions: • How many verified contacts do you want in each priority list? • Should the research cover all U.S. states or specific data-center markets first? I’m ready to begin immediately. Best regards, Carlos.

@ganeshrasekar

I can do it

@Dataentrychanp

Hi, I am Puja Rani. I have read your proposal completely. I have experience in this kind of work I am ready to start working. I am confident that I can handle it easily. If you are interested, please message me and we can discuss further

@AndrewJSMAN

I’ve scraped and validated data-center workforce contacts before—last project covered 1,200+ construction-phase contacts across U.S. sites. Python handles the heavy lifting: I’ll pull active projects from Dodge Data & Analytics, ENR, and county permit logs, then filter for your roles using RCDD, OSP, and construction manager keywords. Scrapy + Selenium for dynamic pages, pandas for normalization, and LinkedIn Sales Navigator API to enrich titles, orgs, and direct dials—no stale data. I’ll split the dataset into your two priority lists, add the selection rationale column, and export a clean Excel with 15+ fields per contact. Turnaround: 5–7 days after scope review. I am experienced with Python Projects. Thanks, Andrii.