Data Solutions Architect

The candidate will have responsibilities across the following functions:

Enterprise Data Discovery and Landscape Assessment:

• Lead comprehensive discovery and assessment of enterprise data estates at large MNC clients - including legacy systems, ERP platforms, operational databases, data warehouses, and unstructured data repositories.
• Reverse-engineer and document data models, schemas, and entity relationships from complex legacy systems and ERP platforms, including SAP (S/4HANA, ECC, BW/4HANA), Oracle EBS/Fusion, and Microsoft Dynamics 365
• Map end-to-end data lineage across heterogeneous source systems, identifying data ownership, quality gaps, duplication, and transformation logic embedded in legacy ETL processes.
• Conduct data profiling and quality assessments to characterise the state of enterprise data assets and surface critical issues that must be addressed before migration or transformation.
• Produce detailed data landscape documentation - source system inventories, data flow diagrams, entity catalogues, and integration topology maps - as inputs to solution architecture design.

Data Ontology and Semantic Modelling:

• Design and develop enterprise-wide data ontologies that standardise entity definitions, relationships, hierarchies, and domain vocabulary across business functions, including finance, supply chain, procurement, HR, and customer management.
• Build formal ontological representations using semantic web standards (RDF, RDFS, OWL) and translate them into actionable data models and integration patterns for enterprise platforms.
• Develop business glossaries, controlled vocabularies, and taxonomies that bridge the gap between technical data structures and business language - enabling consistent understanding across the organisation.
• Design and implement knowledge graphs to represent complex entity relationships and enable advanced querying, reasoning, and AI-driven data discovery across the enterprise.
• Work closely with business domain experts, data stewards, and enterprise architects to validate and evolve ontological models as the business changes.
• Establish ontology governance processes - versioning, change management, stewardship responsibilities, and review cycles - to ensure the enterprise ontology remains accurate and current.

Data Architecture and Solution Design:

• Architect end-to-end enterprise data solutions - spanning data warehouses, data lakes, lakehouses, data mesh, and federated data platforms - tailored to the scale, complexity, and governance requirements of large MNC clients.
• Produce solution blueprints, reference architectures, logical and physical data models, integration patterns, and technology roadmaps that translate business strategy into executable data architecture.
• Design cloud-native data platform architectures on AWS, GCP, or Azure, incorporating cloud-native services for ingestion, storage, processing, governance, and consumption.
• Define data integration architectures for harmonising ERP, CRM, SCM, HR, and legacy system data into unified analytical and operational data platforms.
• Establish data architecture standards, design principles, and guardrails that govern how data solutions are built and evolved across the organisation.
• Lead architecture review boards, conduct solution design reviews, and ensure proposed data solutions meet scalability, security, performance, and governance requirements.

ERP Data Expertise and Integration:

• Design data extraction and transformation strategies for SAP S/4HANA, SAP BW/4HANA, Oracle EBS, Oracle Fusion, and Microsoft Dynamics 365 leveraging both standard and custom extraction methods.
• Address the unique complexity of ERP data - including client/company code structures, multi-currency and multi-language configurations, organisational hierarchies, and deeply nested relational models.
• Design ERP-to-cloud migration and modernisation architectures that preserve data integrity, business logic, and historical context while enabling modern analytics and AI workloads.
• Advise clients on data harmonisation strategies when consolidating multiple ERP instances - typically arising from mergers, acquisitions, or global rollouts.

Master Data Management (MDM) and Data Governance:

• Design enterprise MDM frameworks for critical business entities - customer, product, supplier, material, employee, and location - across heterogeneous source systems and geographies.
• Define entity resolution, deduplication, golden record creation, and survivorship rules for master data domains in complex, multi-source MNC environments.
• Architect MDM platform implementations using tools such as Informatica MDM, SAP MDG, IBM MDM, or cloud-native MDM solutions.
• Design and implement data governance frameworks - including data ownership models, stewardship accountability, policy management, data quality metrics, and audit mechanisms.
• Establish metadata management architectures and data cataloguing strategies using tools such as Collibra, Alation, Apache Atlas, or Microsoft Purview.
• Define data classification schemes, sensitivity labels, and access control models to ensure enterprise data is protected and compliant with regulatory requirements.

Client Advisory and Stakeholder Engagement:

• Engage at CXO, CDO, and enterprise architecture leadership levels at large MNC clients - presenting data architecture visions, programme roadmaps, and solution recommendations with authority and clarity.
• Lead client workshops - including architecture discovery sessions, design thinking exercises, and architecture review meetings - to align stakeholders on data strategy and solution direction.
• Translate complex technical architecture concepts into business-relevant language that resonates with senior business and finance stakeholders unfamiliar with data engineering.
• Build trusted advisor relationships with client data and technology leadership, positioning the organisation as a long-term strategic partner for enterprise data transformation.
• Contribute to presales activities - including RFP responses, solution demonstrations, architecture proposals, and effort estimation for large data transformation programmes.

Innovation and Thought Leadership:

• Stay at the forefront of enterprise data architecture trends - including data mesh, data fabric, AI-ready data platforms, and graph-based data intelligence.
• Evaluate and recommend emerging data technologies, frameworks, and platforms relevant to the challenges faced by large MNC data estates.
• Develop reusable architecture assets - reference architectures, solution accelerators, data models, and ontology templates - that can be applied across multiple client engagements.
• Contribute to the organisation's data architecture practice through internal knowledge sharing, mentoring of senior data engineers, and participation in practice-building initiatives.

The core requirements for the job include the following:

Education and Technical Skills:

• Bachelor's or Master's degree in Computer Science, Information Technology, Information Systems, or a related engineering discipline.
• 10-15 years of progressive experience in data architecture, data engineering, or enterprise information management roles - with at least 5 years in a senior architect capacity.
• Demonstrable, hands-on experience working with large MNC data estates encompassing legacy systems and one or more major ERP platforms: SAP (S/4HANA, ECC, BW), Oracle EBS/Fusion, or Microsoft Dynamics 365
• Deep expertise in data modelling - conceptual, logical, and physical - including dimensional modelling, data vault, and normalised relational models for both OLTP and OLAP use cases.
• Proven experience designing data ontologies, taxonomies, and business glossaries using formal standards (RDF, RDFS, OWL) and ontology tooling such as Protege.
• Strong proficiency in SQL and at least one programming or scripting language (Python, Scala, or equivalent) for data discovery, profiling, and architecture validation.
• Hands-on experience with at least one major cloud data platform - AWS (Redshift, Glue, Lake Formation, S3), GCP (BigQuery, Dataplex, Dataflow), or Azure (Synapse Analytics, Purview, ADLS).
• Experience designing and implementing MDM frameworks and familiarity with at least one MDM platform (Informatica MDM, SAP MDG, IBM MDM, or equivalent).

Good to Have:

• Experience with knowledge graph platforms and graph databases - Neo4j, Amazon Neptune, or Azure Cosmos DB (Gremlin API).
• Familiarity with enterprise architecture frameworks such as TOGAF, Zachman, or equivalent.
• Exposure to data mesh architecture principles and federated data ownership models.
• Knowledge of AI/ML data platform requirements - feature stores, vector databases, training data management - and how they integrate with enterprise data architectures.
• Experience with data fabric platforms such as Informatica IDMC, Talend, or IBM Cloud Pak for Data.
• Familiarity with data observability and data quality tools - Collibra, Alation, Monte Carlo, Great Expectations, or Soda.
• Understanding of data privacy regulations relevant to MNC operations - GDPR, DPDP Act, CCPA - and their implications for data architecture design.
• Cloud architecture certifications (AWS, GCP, or Azure) in data or analytics specialisations.

Soft Skills and Leadership Qualities:

• Exceptional communication and executive presence - able to present complex data architecture concepts with clarity and conviction to CXO and CDO audiences.
• Strong consultative mindset - listens carefully, asks the right questions, and designs solutions that genuinely address business problems rather than applying generic patterns.
• Comfortable operating in ambiguous, high-complexity environments typical of large multinational organisations with long histories and heterogeneous technology landscapes.
• Structured thinker who can decompose large, complex data problems into manageable architectural decisions and phased delivery roadmaps.
• Natural collaborator who works effectively with client teams, technology vendors, delivery managers, and internal data engineering and consulting colleagues.
• Self-driven and intellectually curious - continuously learning and applying new developments in the rapidly evolving enterprise data and AI landscape.