Data Analyst
Description
Senior Business Analyst - Enterprise Data Management
About Enterprise Data Management
EDM operates as a shared enterprise service: we provide the standards, expertise, and capabilities that let the business manage data as an enterprise asset, while the business retains ownership and accountability for its own data.
We run a federated model. Master data is created and stewarded in the operating systems by the functions that own it; EDM provides authoritative-source designation, cross-system linking, quality monitoring, and the standards and tooling that make the whole set hang together. We enable the business to master data in place - we do not master records on its behalf.
The role
The Senior Business Analyst is a hands-on delivery role at the centre of that model. You will work across Master Data Management, Master Data Operations, data quality, impact analysis, and incident root-cause work - translating business needs into data outcomes, and doing the analysis, querying, and reporting that make those outcomes real.
This is a build-and-operate role for someone equally comfortable in a stakeholder workshop, a data model, a SQL editor, and a Power BI canvas.
What you'll do
Master Data Management & Operations (MDM / MDO)
Elicit and document master-data requirements across domains (customer, supplier, material/product, finance) and translate them into authoritative-source designations, golden-record definitions, and cross-system data flows.
Define and maintain match/link, reconciliation, and attribute-level survivorship rules; support the distribution of shared identifiers and reference data to consuming systems.
Partner with embedded data stewards in the operating systems - enabling them to create and maintain records to standard, and routing cross-system conflicts to resolution.
Produce landscape maps, data models, and readiness sign-offs for projects that stand up or change master data.
Governance engagement (EDG triggers)
Recognize the defined events that call for Enterprise Data Governance (EDG) - such as two systems disagreeing on the same entity, a domain with no authoritative source, contested cross-domain ownership, a new creating system needing shared standards, or a policy or regulatory change - and trigger EDG through the engagement catalogue as they arise in day-to-day work.
Route authority, standards, and arbitration decisions to EDG rather than resolving them unilaterally, bringing the operational context and evidence EDG needs to decide.
Track each governance trigger to closure and feed the outcome back into standards and operations.
Data Impact Analysis
Run intake and assessment for projects, changes, migrations, and integrations that touch data.
Trace cross-system impact using lineage and the data catalog; identify affected domains, systems, reports, and downstream consumers.
Rate and prioritize data risks and produce decision-ready recommendations that route work into the right services.
Root Cause Analysis & data incident support
Investigate data incidents across systems - establishing origin, blast radius, and the systemic cause, not just the symptom.
Coordinate remediation with the responsible source owners and feed preventive improvements back into data quality rules and standards so issues are designed out, not re-cleaned.
Data Quality, querying & reporting
Profile data and help develop DQ rules, thresholds, and acceptance criteria; monitor conformance and surface issues to the responsible owners.
Advanced SQL - to profile, join, reconcile, investigate, and validate data across large, multi-system datasets.
Power BI - build and maintain dashboards and scorecards for domain health, quality trends, MDM operational metrics, and executive reporting.
Business analysis fundamentals
Lead requirements gathering, workshops, and stakeholder engagement across business, IT, analytics, and governance partners.
Produce clear, professional documentation: standards, procedures, process maps, and specifications.
Participate in and help run data stewardship Community of Practice workshops and sessions - sharing patterns and reusable solutions, aligning practice across domains, and bringing steward feedback back into standards.
What you bring
Required
5+ years in business analysis, data analysis, or data management, with hands-on master-data and/or data-quality delivery.
Advanced SQL - able to independently profile, join, reconcile, and validate data across large, multi-system datasets.
Strong Power BI - data modeling, DAX, and dashboard/report design for both operational and executive audiences.
Demonstrated experience in impact analysis and/or root-cause analysis on data issues spanning multiple systems.
Ability to translate fluently between business stakeholders and technical teams, and to document to a professional standard.
Comfort operating in a federated model - influencing and enabling data owners and stewards you do not directly control.
Able to recognize defined governance events in day-to-day work and trigger or escalate to Enterprise Data Governance (EDG) through the engagement catalogue - routing authority and standards decisions to EDG rather than resolving them ad hoc.
Preferred / nice to have
Experience with a major MDM platform (e.g., SAP MDG) and/or a data catalog & quality suite (e.g., Collibra).
SAP master-data experience (material, customer, supplier, finance).
CDMP or equivalent data-management certification; Lean Six Sigma exposure.
Experience supporting large migrations (e.g., ECC → S/4HANA) or AI / analytics data-readiness work.
Core competencies
Analytical rigor - gets to the real cause and the real number, and can show the work.
Ownership - drives an item to a signed-off outcome across teams that don't report to you.
Communication - makes data decisions legible to both a steward and a leader.
Bias to enable - builds the standard, the rule, and the tool that let others succeed.
How you'll know you're succeeding
Projects build on designated authoritative sources instead of picking a system at random.
One entity resolves consistently across systems; duplicates fall; identifiers propagate reliably.
Data risks are surfaced before commitment, and incidents are traced to cause and prevented from recurring.
Stakeholders get clear, trusted reporting that they act on.
