Sr. Data Architect

Requirements:

• 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 modeling, data vault, and normalized 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 Protg.
• 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 equivalents.
• 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 specializations.

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 organizations 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.