n8n Automation Builder for Dental Scorer

I am looking to hire an experienced n8n automation builder and Python/API developer at $45/hour to build a private internal MVP for a Dental Practice Opportunity Scorer. I prefer to work hourly, but I want the project managed with clear milestone estimates, approval before moving beyond each milestone, and no open-ended billing. Please include your estimated hours for each phase, recommended data providers, expected third-party costs, and examples of similar automation/API/data workflow projects. The first paid step will be a technical planning milestone capped at 5–8 hours. For this phase, I want a recommended architecture, data providers, database/output layer, n8n workflow structure, scoring approach, expected limitations, and estimated hours for the full MVP build. Project Overview: The goal is to scan a selected city, ZIP list, or radius for dental practices, collect public business data using existing tools/APIs, enrich each record with basic online signals, calculate an explainable opportunity score, and output a ranked lead list. This is not a public SaaS app. It is a private internal lead intelligence workflow. I want to build from scratch as little as possible by using tools like Apify, Outscraper, DataForSEO, SerpApi, Google Places API, PageSpeed Insights, Wappalyzer/BuiltWith, or similar providers where appropriate. n8n should act as the workflow controller. Airtable, Supabase/Postgres, or Google Sheets can be used for the database/output layer. The first version should be practical, easy to run, and easy to adjust. Core Workflow: - Enter a market: city/state, ZIP list, radius, or search area. - Collect dental practices using an existing data/scraping provider. - Store raw and cleaned records. - Deduplicate by phone, domain, address, place ID, and name similarity. - Enrich records with basic website, review, service, and local visibility signals. - Calculate an editable Opportunity Score with A/B/C/D tier, confidence level, score drivers, and suggested pitch angle. - Output a ranked lead table with filters/export options. - Allow optional deeper analysis only for selected A-tier or strong B-tier practices. Scoring Requirements: - Rule-based or mostly rule-based at first, not a black-box AI score. - Weights should be easy to edit later. - Each score should show clear reasons. - Missing or weak data should lower confidence instead of creating false certainty. - The first-pass score should be simple and directional, not a full SEO audit. Suggested Milestones: 1. Technical Plan — 5–8 hours Confirm provider, database, fields, workflow map, costs, risks, and build plan. 2. Market Scan + Storage — 8–15 hours Collect dental practices from one test market and store normalized records. 3. Enrichment + Deduplication — 10–20 hours Add website, review, service, and basic local visibility signals, then clean and deduplicate records. 4. Scoring + Ranked Output — 8–15 hours Build the scoring model, A/B/C/D tiers, confidence, score drivers, pitch angle, and export/filter options. 5. Handoff + Optional Deep Analysis — 3–6 hours Provide documentation, walkthrough, setup instructions, and optional selective deep-analysis workflow if in scope. Expected Deliverables: - Working n8n market scan workflow. - Normalized lead database/table with raw data references. - Deduplication logic. - Website/conversion/service signal checks. - Review intelligence summary where available. - Editable opportunity scoring engine with visible reasons. - Ranked lead table with filters/export options. - Documentation and handoff video or live walkthrough. What This Project Is Not: - Not a polished public SaaS app. - No user accounts, billing, or multi-tenant permissions in Phase 1. - No automated cold email or SMS sending in Phase 1. - No full SEO audit for every practice. - No deep analysis on every record. - No fully custom scraper unless existing tools cannot provide the needed data. Preferred Freelancer: - Strong n8n experience with APIs, error handling, and workflow documentation. - Python/API experience for enrichment, website checks, scoring, and data cleanup. - Experience with Apify, Outscraper, DataForSEO, SerpApi, Google Places API, or similar tools. - Comfortable using Airtable, Supabase/Postgres, or Google Sheets. - Understands this is a lead intelligence system, not just a scraper. Please respond with: - Recommended architecture in 5–10 bullets. - Data providers you recommend and why. - Database/output recommendation. - Estimated hours for each milestone. - How you would keep scoring simple, editable, and explainable. - Third-party costs I should expect. - Relevant examples of similar work. - Risks or limitations I should understand before starting.