Data Center Contacts Compilation

@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data-center contact research with a strong focus on **recent, relevant and project-specific contacts**, rather than outdated lists.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 • Perform a final quality check to remove duplicates, outdated/relevantity issues and incomplete records.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, "Why was this contact selected?" 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.

@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. Regards, Imran

@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.

@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.

@albertc1996

Hey, I hope you're doing great! Which US states or regions should the research prioritize for the active data center projects? What source date or recency standard should qualify a contact as an excellent recent contact? How many contacts would you like in each of the under-construction and planned lists? I am experienced with Python Projects, and I have 7 years of experience building data collection and management workflows with Node.js, Express, MongoDB, and PostgreSQL. I have worked on structured datasets, research tools, and web-based systems where accurate records and clear selection reasons matter. I can organize the Excel file with the requested company, address, URL, phone, social profiles, role, project status, and why each contact was selected. I would be glad to help with this research.

@engruhulajom

Hello There! I’m Md Ruhul Ajom, and I’m excited to partner with you. I have rich experience in Python-based data research, web research, lead generation, contact verification, data extraction, and building clean Excel databases. I understand you need fresh, recent contacts tied to active U.S. data-center projects, separated into under-construction and planned projects, with priority given to decision-makers involved in fiber, OSP, telecom, infrastructure, engineering, procurement, and construction. I’m skilled in Python, web research, data scraping, contact verification, LinkedIn research, Excel, data cleaning, and structured lead generation. I have some questions: 1) Should the research cover all U.S. states, or do you have priority states/markets for data-center development? 2) How recent should a project/contact activity be—for example, activity within the last 30, 60, or 90 days? 3) Should I include only publicly verifiable direct emails/phone numbers, or also clearly identified professional contacts where direct details require additional verification? I’m ready to start immediately and can provide separate, clean Excel lists with the “Why was this contact selected?” column and all requested company/contact details. Looking forward to hearing from you. Best regards, Md Ruhul Ajom

@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.

@huonghy89

Hi, I hope you are doing well. Very happy to bid your project because my skills are fitted in your project. I am experienced with Python Projects. I have strong experience with Python-based web research, data collection, validation, lead enrichment, and building structured B2B contact datasets from current public sources while checking company, role, project, and contact relevance. I will research two separate USA data-center lists—facilities currently under construction and facilities that are planned—with priority on active projects involving high-count fiber, OSP, telecom, network infrastructure, engineering, construction, and procurement. I will identify relevant current professionals in your specified roles and organize verified public business information including company, project/location, job title, business contact details where publicly available, company URL, LinkedIn/social references, and source information. I will also include a “Why was this contact selected?” column explaining each contact’s connection to the active data-center/fiber project and prioritize recent, verifiable information rather than outdated lists. If you send the message, we can discuss the project more. Thanks.

@dataspro

Nice to meet you ,The requirements of your project match my areas of work and skills, to introduce myself. My name is Anthony Muñoz and i am the lead engineer for DS Pro IT agency. I have worked for over 10 years as a Full-Stack and software development engineer and have successfully done multiple jobs. It will be a pleasure to work together to make your project. Feel free to discuss about the project with me, greetings.

@KasjanovaEste

I looked at your brief and saw you need a Python‑driven contact compilation where fresh, project‑active data‑center professionals are identified, validated and organized into two lists—under‑construction and planned—along with full company details and a column explaining why each contact was selected. I am experienced with Python Projects and will scrape, filter and verify recent contacts, classify roles, build the Excel with all required fields, and ensure selections match fiber‑cable project relevance. I can start right away. Fernando PS. Have question on the project and I would like you to check the clarification board

@robertb297

I am experienced with Python Projects. Hi, I can build a recent, highly targeted U.S. data-center contact database focused specifically on the fiber cable market. I’ll research active data-center projects and separate them into **under construction** and **planned** lists, then identify relevant decision-makers across construction, infrastructure, telecom, OSP, engineering, procurement, and network roles. The Excel file will include company, project, contact, role, address, website, phone, project status, and a **“Why was this contact selected?”** column explaining the relevance of each contact. I’ll prioritize recent, verifiable contacts connected to active projects and keep the final spreadsheet clean, structured, and ready for outreach. **One key question:** would you like me to prioritize specific U.S. states, or cover the entire country based on the strongest active projects? I’m ready to start with the highest-priority projects and build the list around the most relevant decision-makers.

@thomasb726

Hello, High-risk contact lists waste outreach budget when entries are outdated or mis-targeted; for high-count fiber cable sales you need recent, role-specific contacts at data centers "under construction" and "planned" with verified responsibility for OSP, fiber-installation, procurement, and project execution. I am experienced with Python Projects I have led data-heavy integrations and contact enrichment pipelines as a Senior Software Engineer and CTO, including projects that combined API ingestion, deduplication, and recent-verification workflows for enterprise clients (payroll API integration; mission-critical tax filing system). I apply that same data quality discipline to contact compilation: timestamped sourcing, role-normalization (Project Executive, OSP Construction Manager, RCDD, Director of Infrastructure), and accuracy checks against construction permit records, vendor rosters, and recent LinkedIn/contractor confirmations. I will start by building a Python pipeline that ingests multiple source sets, normalizes job titles to your priority lists, validates "under construction" vs "planned" status from construction filings and public announcements, and outputs deduplicated, timestamped contacts with source confidence scores. - To be considered, please tell me "I am experienced with Python Projects" I am experienced with Python Projects - Do you have geographic limits (national, state, or metro priorities) or target operators/colocation providers to exclude or focus on? - What delivery format do you prefer (CSV with source URLs and confidence score, or a relational export for direct import into your CRM)? Open to a quick chat if you want to dig into the details. Thomas Beigbeder