1. Role Summary

We are looking for a senior Data Architect to lead the data cleansing and harmonization workstream for an AI for Spares program within Purchasing.

The role will define the target data architecture and establish a trusted, standardized data foundation across material master, inventory, contracts, catalogs, supplier references, and purchasing history.

The Data Architect will collaborate with Purchasing, Maintenance, Engineering, Warehousing, enterprise data teams, SAP teams, AI teams, and implementation partners to prepare procurement data for semantic search, duplicate detection, equivalent-part matching, inventory reuse, and AI-guided buying.

2. Key Responsibilities

A. Data Architecture & Roadmap

- Define and own the target data architecture for the AI for Spares program, aligned with enterprise architecture and procurement transformation priorities.
- Develop a phased data roadmap covering discovery, pilot, rollout, and continuous improvement, including source onboarding, cleansing, harmonization, integration, governance, and operational support.
- Define canonical data models for materials, spare parts, technical attributes, inventory, contracts, catalogs, supplier references, and historical purchasing data.
- Document data flows, system boundaries, lineage, refresh models, interfaces, dependencies, and non-functional requirements.

B. Data Cleansing, Standardization & Harmonization

- Lead the profiling and remediation of duplicate, obsolete, incomplete, inconsistent, and low-quality material and spare-parts records.
- Define cleansing and standardization rules for descriptions, manufacturer and supplier part numbers, units of measure, technical characteristics, brands, product families, and local codification.
- Design cross-reference and golden-record approaches for identifying identical, similar, interchangeable, and technically equivalent items across plants and ERP systems.
- Establish repeatable data-quality controls, exception handling, approval workflows, and remediation backlogs.

C. Taxonomy, Classification & Semantic Data

- Design and govern a harmonized spare-parts taxonomy and classification model across relevant product categories and business units.
- Define mandatory technical attributes, naming conventions, metadata standards, and multilingual description rules by material family.
- Prepare data structures for semantic search, fuzzy matching, embeddings, knowledge graphs, and other AI-enabled matching approaches, where appropriate.
- Partner with category experts, maintenance engineers, and data stewards to validate classification and equivalence rules.

D. Data Integration & Platform Architecture

- Design ingestion and integration patterns across SAP S/4HANA, SAP ECC, SAP MDG, Ivalua/digiPACE, Azure Data Lake, catalogs, contracts, inventory repositories, and approved external data sources.




- Define batch, API, event-driven, and file-based integration approaches, including data refresh, reconciliation, error management, and traceability.
- Ensure the architecture supports plant, legal entity, purchasing organization, category, language, and authorization boundaries.
- Collaborate with solution architects and engineering teams to translate architecture into scalable implementation designs.

E. AI Readiness & Solution Enablement

- Prepare complete, standardized, and explainable data products for AI-guided requirement capture, material search, inventory visibility, contract and catalog matching, and recommendation engines.
- Define training, validation, test, and reference datasets with clear provenance and quality thresholds.
- Work with AI suppliers and internal teams to evaluate matching quality, false positives, false negatives, confidence thresholds, and explainability.
- Ensure AI recommendations are traceable to trusted sources, governed rules, and approved data.

F. Data Governance, Security & Compliance

- Define data ownership, stewardship, approval rights, issue escalation, and governance cadence for procurement master and reference data.
- Establish data-quality KPIs and dashboards covering completeness, validity, uniqueness, consistency, timeliness, and match accuracy.
- Ensure compliance with GDPR, internal data classification, data residency, retention, access-control, auditability, and security requirements.
- Implement sustainable controls to maintain data quality and reliability beyond the initial remediation phase.

G. Delivery, Collaboration & Communication

- Lead data discovery and design workshops with business and IT stakeholders across countries and entities.
- Manage data assumptions, risks, dependencies, decisions, and acceptance criteria for the program.
- Guide data analysts, data engineers, SAP specialists, master-data teams, and external partners.
- Produce architecture diagrams, mapping specifications, data dictionaries, quality-rule catalogs, governance documentation, and executive updates.

3. Key Deliverables

- Target data architecture and approved source-to-target data model.
- Data inventory, profiling assessment, quality baseline, and prioritized remediation backlog.
- Harmonized spare-parts taxonomy, attribute model, naming standards, and data dictionary.
- Duplicate and equivalent-item matching framework, including confidence thresholds and human-validation rules.




- Source-to-target mappings and integration specifications for in-scope platforms.
- Golden-record, cross-reference, and lineage model across plants and ERP environments.
- Data-quality scorecards, governance model, stewardship workflows, and continuous-improvement process.
- AI-ready datasets and acceptance criteria supporting pilot and rollout use cases.

4. Required Skills & Experience

Must-Have Technical Skills

- Enterprise Data Architecture.
- Data Cleansing, Standardization, and Harmonization.
- SAP Material Master and SAP Master Data Governance (MDG).
- Data Quality Management and Data Governance.
- Taxonomy, Classification, and Reference Data Management.
- Cross-ERP Data Mapping and Integration.
- Duplicate Detection, Entity Resolution, and Equivalent-Item Matching.
- Microsoft Azure Data Platform.
- Procurement, Inventory, Supply Chain, and Spare-Parts Data.

Nice-to-Have Skills and Certifications

- TOGAF certification.
- DAMA CDMP certification.
- SAP MDG certification.
- Microsoft Azure Data Engineer certification.
- Microsoft Azure Solutions Architect certification.

5. Academic Qualifications

- Bachelor's or Master's degree in Computer Science, Information Systems, Data Management, Engineering, or a related field.

6. Experience Requirements

- 12+ years of experience in data architecture, data engineering, consulting, or Master Data Management, including significant responsibility for enterprise data transformation.
- Proven delivery of at least two complex data cleansing or harmonization programs across multiple business units, countries, or ERP systems.
- Solid knowledge of SAP Material Master, SAP MDG, SAP S/4HANA, and SAP ECC data structures and governance processes.
- Hands-on understanding of data profiling, standardization, deduplication, record linkage, survivorship, golden records, and data-quality rule design.
- Experience designing taxonomies, classification models, technical attribute schemas, metadata, and reference-data standards.
- Experience with procurement, inventory, maintenance, supply chain, catalogs, contracts, and spare-parts data in an industrial or asset-intensive environment.
- Experience designing data solutions on Microsoft Azure, including data lake, integration, transformation, metadata, lineage, quality, and analytics capabilities.
- Understanding of semantic search, fuzzy matching, entity resolution, embeddings, vector search, knowledge graphs, and AI/ML data preparation.
- Ability to define measurable quality thresholds and translate data issues into business impact, delivery priorities, and remediation plans.
- Excellent communication, facilitation, documentation, and stakeholder-management skills in a multicultural environment.

7. Language Requirements

Advanced English proficiency, both written and spoken

📌 Data Architect – Procurement Data, AI for Spares (Maharashtra)
🏢 TuTeck Technologies
📍 Maharashtra