CRM / Relationship Database Cleanup Contractor

Clean, consolidate, and prepare contact and company data from a fragmented source landscape for import into a new relationship management system (Real Deal). You're working with data scattered across multiple CRMs, Gmail/Outlook contacts, iPhone backups, spreadsheets, and other fragmented sources — your job is to turn it into a unified, accurate, deduplicated foundation that actually works.

The data quality here determines the quality of the relationship management system. You're the last line of defense before import.

What You'll Do

• Source Inventory: List all contact and company data sources (CRMs, email accounts, contact apps, spreadsheets, exports, etc.) and assess data size, quality, and overlap.

• Schema & Priority Rules: Create a unified field structure and establish source-of-truth rules to determine which system takes priority for fields such as email, phone, title, company, relationships, and notes when conflicts occur.

• Do-Not-Merge Rules: Define clear protocols for records that should never be automatically merged (e.g., spouses, assistants, family members, investors with shared companies, or individuals with similar names).

• Extraction & Consolidation: Extract data from all sources and combine it into a single unified working dataset.

• Deduplication: Identify and flag obvious duplicate records for merging while routing ambiguous cases for human review rather than forcing bulk merges.

• Relationship Context Preservation: Retain relationship mappings, tags, assistant relationships, company affiliations, notes, and source history where available.

• Standardization & Enrichment: Standardize name formats, normalize phone numbers and email addresses, align company associations and job titles, and enrich records with LinkedIn profiles and company information where possible (with enriched fields flagged for review).

• Import Schema Alignment: Map the finalized dataset to the Real Deal import structure, including required fields, optional fields, tags, company associations, and review indicators.

• QA & Validation: Conduct quality assurance on a statistically meaningful sample, reviewing duplicate accuracy, field completeness, company association accuracy, and import readiness.

• Audit Trail & Documentation: Document all actions and decisions, including merged records, flagged items, archived data, enrichment updates, and outstanding items requiring review.

• CRM cleanup experience at scale — you've cleaned contact data across multiple sources and CRMs, not just one-off lists
• Deduplication judgment — you understand the difference between "obviously duplicate" and "probably the same person but risky to merge." You don't auto-merge everything
• Comfort with fragmented, incomplete data — missing fields, conflicting information, inconsistent structures don't faze you
• Data sensitivity — you handle relationship data with discretion and follow strict access, privacy, and confidentiality protocols
• AI-fluency for this work — you use Claude or similar tools to accelerate standardization, enrichment, and validation without sacrificing accuracy
• Source-priority thinking — when the same person has different emails/phones/titles across Gmail, Outlook, iPhone, and CRM exports, you have a system for deciding which is authoritative
• Audit trail discipline — you document decisions, build review queues, and leave a clear record of what was done and why

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