Data Migration
Description
Moving data from legacy systems to new platforms as part of software implementations. Involves discovery, mapping, extraction, transformation, validation, and reconciliation—ensuring data integrity and business continuity during system transitions.
Proficiency Breakdown
| Dimension | Level | Notes |
|---|---|---|
| Theoretical knowledge | ⭐⭐⭐⭐☆ | Migration methodology |
| Practical application | ⭐⭐⭐⭐⭐ | 100+ migrations |
| Teaching ability | ⭐⭐⭐⭐☆ | Created intake playbooks |
| Industry currency | ⭐⭐⭐⭐⭐ | Essential implementation skill |
Self-assessment: Advanced. Led data migration activities across 100+ implementations with post-go-live defect rate of ≤0.7 per 1,000 migrated records. Created conversion intake playbooks that improved data sign-off time by 37%.
Evidence & Proof Points
Specific Accomplishments
- Led data conversion activities across 100+ implementations
- Created Conversion Intake playbook with standard checklist improving sign-off time 24→15 days (37%)
- Achieved post-go-live conversion defects ≤0.7 per 1,000 migrated records
- Coordinated offense code mapping across legacy systems, municipal codes, and state vehicle codes
- Partnered with technical teams on data validation and reconciliation
Quantifiable Results
| Metric | Value | Context |
|---|---|---|
| Sign-off improvement | 37% | 24→15 business days |
| Defect rate | ≤0.7/1,000 | Post-go-live |
| Migrations led | 100+ | Across implementations |
Resume Presence
Appears in: 7/25 variants
Variants: Veritone, Anthropic, IL SOS, OpenGov IC, OpenGov PM, TransUnion, PayPal
How I typically phrase this skill:
“Led data migration activities including discovery, mapping, validation, and conversion—achieving defect rates ≤0.7 per 1,000 migrated records”
Variations by context:
| Context | Framing |
|---|---|
| Technical audience | ”Managed data conversion: intake, mapping, extraction, transformation, validation, and reconciliation” |
| Non-technical audience | ”Ensured customer data transferred accurately and completely to new systems” |
| Leadership focus | ”Created conversion playbooks improving sign-off time 37% while maintaining <0.1% defect rate” |
Demonstrated in Projects
Primary evidence:
- Conversion Intake Optimization — Streamlined data conversion, 37% improvement in sign-off time
- 100+ Municipal Court Implementations — Managed data migrations across 100+ courts
- Illinois Vehicle Code Reconciliation — Complex data reconciliation project
Supporting evidence:
- SAP EAM Implementation Support — Data migration support for SAP EAM
Related Skills
Parent/Umbrella
Sibling Skills (often paired with)
Sub-skills/Specializations
- Data mapping
- Offense code reconciliation
- GL mapping
- Test data validation
- Reconciliation reporting
Interview Notes
Common questions about this skill:
- “Walk me through your data migration process.”
- “How do you handle data quality issues during migration?”
- “What’s the most complex migration you’ve managed?”
Your STAR story:
- Situation: Data conversion sign-off taking 24 business days, delaying go-lives
- Task: Streamline conversion process without sacrificing quality
- Action: Created intake checklist with clear owners and mappings, introduced early sample extracts, established mapping workshops
- Result: Sign-off time improved 24→15 days (37%); defect rate maintained at ≤0.7/1,000
ATS Keywords
Data Migration, Data Conversion, ETL, Data Import, Legacy Migration, Data Mapping, Data Validation, Data Quality, System Migration