Senior Data Architect (Enterprise Finance/Operational)

About the project

Andersen is hiring a Senior Data Architect (Enterprise Finance/Operational) for a project designing scalable data architecture and supporting secure AI transformation in the healthcare sector.

The customer is a global consulting and advisory firm that helps organizations improve business performance, navigate complex operational challenges, and support strategic transformation. It provides expertise across business strategy, operational improvement, risk management, and organizational change, working with clients from multiple industries through data-driven insights and multidisciplinary consulting services.

The project is focused on designing a scalable enterprise data architecture to support a large-scale healthcare AI transformation. It includes assessing the existing data landscape, defining future-state data architecture, and developing governance, integration, and modernization roadmaps to enable AI and advanced analytics

Responsibilities

• Assessing enterprise data landscape across financial, operational, inventory, and ERP systems (~9 ERP environments consolidated through acquisitions).
• Identifying trusted data challenges, reporting inconsistencies, and data silo issues resulting from acquisitions, including month-end close delays and lack of central spend visibility.
• Defining future-state enterprise data strategy supporting ERP modernization and AI initiatives.
• Establishing recommendations for centralized enterprise data platforms (unified data platform / EDW 2.0 — consolidating governed enterprise data across ERP, EMR and specialized systems, not replacing systems of record).
• Designing integration architecture supporting multiple ERP environments, based on a canonical/semantic layer, event backbone and API-first integration spine.
• Defining master data management strategy across enterprise financial and operational domains, with concrete consolidation targets (chart of accounts ~80→1, bank accounts ~900→<200, item and vendor master).
• Developing enterprise ETL/ELT and data consolidation approaches, including an AI-powered ETL pattern (Discover → Cleanse → Map → Transform → Validate → Migrate).
• Recommending semantic and business data models supporting analytics and decision-making.
• Defining data governance operating model as a deliverable in its own right, aligned to the client's new operating model.
• Establishing metadata, lineage, master data, and data quality recommendations.
• Supporting trusted reporting and enterprise visibility requirements.
• Defining enterprise standards for financial and operational reporting consistency.
• Assessing enterprise data readiness for AI adoption (distribution statistics, semantic/feature layer).
• Developing recommendations for AI-ready data architecture.
• Evaluating opportunities for centralized data platform approaches supporting future AI capabilities.
• Collaborating with AI Architects and Enterprise Architects.
• Producing migration-risk assessment with Architecture Decision Records (ADRs) for key data decisions.
• Collaborating with AI Architects and Enterprise Architects as one integrated team (Andersen + BRG), building one cohesive story where the data assessment informs the architecture and roadmap.
• Producing executive-level recommendations and data modernization roadmaps.
• Communicating to a dual audience: explain AI-enabling data concepts simply to a traditional, non-AI-minded corporate-finance team, while remaining defensible to sophisticated AI-forward (PE-level) stakeholders.
• Working in a "validate, don't discover" posture: build on the corporate-finance team's existing discovery, identify meaningful gaps, and translate findings into practical recommendations with explicit caveats (assumptions / risks), operating on samples without full technical access.
• Assessing trusted-data challenges across acquired business units.
• Defining master data strategies supporting inventory, procurement, spend management, and finance operations.
• Developing recommendations for enterprise-wide visibility across operational and financial reporting processes.
• Recommending approaches supporting month-end close optimization and reporting modernization.

Requirements

• Experience in enterprise data architecture for 8+ years.
• Strong MDM expertise , ideally at enterprise scale (large-scale COA / bank-account / item / vendor master consolidation).
• Experience consolidating data from multiple business units, acquisitions, or ERP systems.
• Strong understanding of enterprise reporting and analytics architectures , including trusted data and month-end close.
• Experience with finance, operational, inventory, or supply chain data domains , including procurement and spend management.
• Data governance framework development experience.
• Strong consulting and stakeholder management skills , with the ability to translate AI/data complexity for a traditional, non-AI-minded finance audience.
• Experience conducting maturity assessments and enterprise discovery initiatives.
• Working posture of "validate, don't discover" — comfortable working off existing client discovery and samples, without deep technical access, and delivering recommendations with explicit caveats.
• Level of English – from Upper-Intermediate+ and above.

Nice to have

• Azure Data Factory.
• Fabric.
• Snowflake.
• Databricks.
• Data lake / lakehouse platforms.
• Medallion architecture.
• Healthcare data experience/RCM (revenue cycle management).
• HL7/FHIR knowledge, EMR/EHR, HIPAA.
• SOX / AI-audit awareness.

Why join us

• Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc..
• The opportunity to change the project and/or develop expertise in an interesting business domain.
• Job conditions – you can work both fully remotely and from the office or can choose a hybrid variant.
• Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee.
• The opportunity to earn up to an additional 1,000 USD per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities.
• Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated.
• Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies).
• Certification compensation (AWS, PMP, etc).
• Referral program.
• Private health insurance and compensation for sports activities.