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# Data Migration Engineer

- **Company:** Experis
- **Location:** Reading, UK
- **Contract:** Permanent contract
- **Skills:** Data Auditing, Data Cleansing, Information Engineering, Data Migration, Data Mining, Data Profiling, Python (Programming Language), Metadata, Microsoft SharePoint, SQL*Plus, SQL Databases, Unstructured Data, Scripting, Data Lineage
- **Published:** August 26, 2026
- **Apply:** https://www.totaljobs.com/job/data-migration-engineer/experis-job107901534

## About the Role

* Strong data extraction and scripting skills (SQL plus a scripting language such as Python) - this is a scripted, repeatable-process role first, with manual review reserved for genuine exceptions
 * Experience with data profiling, cleansing, de-duplication, and metadata/schema mapping across disparate, messy sources
 * Comfort reconciling structured and unstructured data from multiple systems into one clean data set
 * A methodical, detail-obsessed approach - this role is about data accuracy, traceability, and repeatability, not shortcuts
 * Good judgement on when to escalate rather than infer, especially with ambiguous, incomplete, or sensitive records
 * Experience handling confidential or commercially sensitive data professionally and securely

## Description

Company is building a brand-new internal contract hub to replace its legacy contract tracking tools. Ahead of the technical build, they need a data-savvy analyst to lead a time-boxed project: extracting, profiling, and migrating their entire back-catalogue of executed contracts into the new repository - cleaned, de-duplicated, and mapped against a proper metadata schema.

This is a data quality and data engineering problem as much as anything else. You'll be working across multiple disconnected sources - eSignature platforms, a legacy SQL database, SharePoint, and old ticketing systems - to build a single, reconciled, trustworthy dataset.

What You'll Be Doing

 * Source discovery and profiling - inventory every source system, produce actual record/document counts, and assess data quality, access routes, and risk (week one deliverable)
 * Scripted extraction - write and run scripts (SQL and beyond) to pull documents and structured/unstructured metadata in bulk from eSignature platforms, a legacy SQL database, SharePoint/shared drives, and a contract management system
 * Data cleansing and de-duplication - consolidate everything into a single staging dataset, applying a documented, defensible rule for which record wins where duplicates exist
 * Metadata schema and mapping - build and populate a metadata register against agreed core fields (contract type, counter party, entity, effective/end/renewal dates, value, governing document reference, source system), explicitly flagging gaps rather than inferring or leaving blank
 * Validation via sample migration - run a sample batch into the live repository to test and refine the mapping logic and transformation rules before scaling up
 * Full-scale load - execute the bulk migration into the new repository structure, correctly mapped against the agreed schema and naming convention
 * Documentation and handover - produce a clear data lineage/handover note: what was migrated, what couldn't be and why, anomalies parked for decision, and the scripts and logic used, so the pipeline is repeatable and auditable


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