Data Analyst @ BRP

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.