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The Citation Group
Head of Data MigrationThe Citation Group • Wilmslow, England, United Kingdom
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Head of Data Migration

Head of Data Migration

The Citation Group • Wilmslow, England, United Kingdom
14 days ago
Job type
  • Permanent
Job description

Job Tittle: Head of Data Migration

Role Location: Hybrid (Wilmslow)


Citation Group is a collective of businesses dedicated to supporting small and medium-sized enterprises across a range of essential services. We know that running a business means juggling a lot, from keeping customers happy and staying compliant, to building strong teams and growing sustainably. Our mission is to ease these pressures by providing expertise, guidance, and solutions that enable business leaders to focus on strategic success. From HR and Health & Safety to Cybersecurity, E-Learning, and ISO compliance, we’ve got you covered.


Citation has achieved strong growth through a combination of organic expansion and strategic acquisitions, continually broadening our expertise, services, and reach to create a one-stop shop that supports businesses across the UK, Canada and Australia.


Role purpose

Data migration is the critical path on almost every integration and platform consolidation we run. The engineering rarely fails. What fails is finding data quality problems too late to remediate them, business stakeholders not understanding how their data will land in the new system, and UAT that doesn't genuinely test the migration before go-live.


This role owns data migration end to end — not as a series of one-off projects, but as a permanent discipline. You will turn migration from the thing that blocks our programmes into something we do predictably, repeatably and well. You'll start hands-on, directing engineers across programmes, and build a dedicated migration capability, playbook and team behind you.


Why this role exists

We move data between systems constantly, and we will only do more of it. Today, migration risk is rediscovered programme by programme: quality issues surface during cutover rather than discovery, stakeholders meet their migrated data for the first time in production, and test plans check that records moved rather than that the business can operate on them. We need a single owner who closes those gaps by design, every time, and leaves a reusable playbook in their wake.

Scope

  • Operates across the portfolio — multiple brands, multiple geographies, and successive platform consolidations and acquisitions.
  • Initially a player-coach: personally leads migrations and directs programme and squad engineers. Builds a dedicated migration function and team over time.
  • Owns the migration workstream from discovery through cutover and post-go-live reconciliation on each programme, and owns the migration methodology across all of them.


Key accountabilities


Own the migration approach. Define and own the end-to-end methodology — discovery, profiling, mapping, build, reconciliation, cutover and fallback — and codify it into a playbook that every programme follows. Set the standards, tooling and patterns so each migration starts from a known position rather than a blank page.


Find data quality issues early. Lead a structured data discovery and profiling phase at the front of every migration. Surface quality, completeness and integrity problems while there is still time to remediate them, quantify the remediation effort, and drive the cleanup with data owners before it becomes a cutover crisis.


Own source to target mappings : Own the source-to-target mapping and system compatibility analysis. Go beyond field-level mapping to build a genuine understanding of how the source and target systems each model, validate, and process data — their rules, constraints, and structural differences. Identify and document conceptual mismatches between the two platforms during discovery, so that behavioural gaps are design decisions made in advance rather than defects discovered in test.


Own the environments : Ensure we have appropriate environments to be able stage and test data migrations


Set the business up to test. Make sure stakeholders understand how their data will migrate, in their language, before it happens. Design UAT so it tests the business outcome and not just the record count — representative data, reconciled balances, realistic scenarios, clear test scripts and expected downstream behaviour— so sign-off is meaningful and defects are caught before go-live.


Lead delivery. Own the migration as a workstream on each programme: plan, sequence, direct the engineers building the transformations, run rehearsals and cutover, manage reconciliation and fallback. Stay close enough to the data to challenge the build, spot anomalies and make the call when something looks wrong.


Build the capability. Stand up the playbook, tooling, reusable assets and standards that make migration repeatable. Grow and lead a migration team as volume justifies it, and raise the wider organisation's data-quality and migration literacy.


First 12 months — what good looks like

  • A documented, adopted migration playbook that every active programme is working to.
  • Data quality issues on live programmes are being surfaced and remediated in discovery, not at cutover.
  • Business stakeholders on at least one major consolidation report that they understood the migration and could test it with confidence.
  • A clear plan — and the first hires or named resources — for the standing migration capability.
  • A measurable reduction in migration-driven slippage and post-go-live data defects.
  • Minimal new requirements surfaced during testing


Essential experience

  • Has personally owned complex data migrations end to end, including dat mapping, consolidations between systems (CRM, ERP, HR/payroll or comparable platforms of record).
  • Has led migration as part of larger integration, consolidation or acquisition programmes, working across multiple stakeholder groups.
  • Has run the discovery-and-profiling-led approach that catches quality issues early, and can show how it changed an outcome.
  • Has designed and run UAT for migrations with non-technical business stakeholders, not just delivered data into an environment.

Essential skills — technical

  • Strong, current data skills: SQL, an understanding of ETL/ELT patterns and tooling, and data profiling — credible enough to interrogate data, design the migration approach and direct engineers, even if not writing every transformation personally.
  • Solid data modelling literacy: can read and reason about source and target schemas, relationships, keys and the failure modes between them.
  • Comfortable with reconciliation, cutover planning, rehearsal and fallback as first-class disciplines.

Essential skills — leadership and influence

  • Can translate between the data and the business — gets sceptical, non-technical stakeholders to understand, engage and own their part.
  • Brings calm, structured delivery leadership to high-pressure cutovers and competing priorities.
  • Can build a discipline from scratch: standards, playbooks, tooling and, in time, a team.
  • Drives data owners to remediate quality issues without owning every system themselves — influence over authority.

Desirable

  • Experience in a multi-brand, multi-geography or PE-backed environment with successive consolidations.
  • Migration experience with Salesforce and/or major ERP/HR platforms.
  • Exposure to regulated or data-sensitive contexts (data protection, accreditation, audit).
  • Has built a migration practice or capability before, not just delivered individual migrations.

Behaviours — how we'll know you're the one

  • You treat data quality as the work, not a precondition for the work.
  • You assume the business doesn't yet understand the migration, and you see it as your job to fix that.
  • You design for the migration you'll have to do ten more times, not just this one.
  • You'd rather find the problem in discovery than be a hero at cutover.



Own the source-to-target mapping and system compatibility analysis. Go beyond field-level mapping to build a genuine understanding of how the source and target systems each model, validate, and process data — their rules, constraints, and structural differences. Identify and document conceptual mismatches between the two platforms during discovery, so that behavioural gaps are design decisions made in advance rather than defects discovered in test.

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Head of Data Migration • Wilmslow, England, United Kingdom

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