Data Architect

We are looking for an 'Data Architect' for our client.

Centralizing Data Architecture: Migrating scattered data from various systems into a centralized structure, building an optimized and enriching data architecture tailored for Artificial Intelligence (AI) and Machine Learning (ML) processes.

Single Source of Truth: Establishing "Single Source of Truth" standards for cross-system data sharing; ensuring that customers are represented uniformly and consistently across all systems, regardless of their acquisition channel.

Data Integrity Security: Designing secure data sharing protocols, as well as creating and deploying systemic rules to prevent data duplication.

Data Governance: Defining Data Governance rules, guiding technical teams in their implementation, and ensuring continuous auditing and compliance.

Regulatory Compliance: Designing a data architecture that is 100% compliant with legal frameworks and audit standards such as KVKK (Turkish Personal Data Protection Law) and GDPR; agilely adapting the architecture to any regulatory changes.

Cross-Functional Liaison: Acting as a bridge between highly technical software/infrastructure teams and business end-users, serving as a guide who fluently speaks and translates the language of both sides.

nitelikler

Domain Expertise: In-depth knowledge and profound experience in data architecture, data governance, and data strategy development.

Project Experience: Active, end-to-end involvement in Data Warehouse projects and the transition from traditional to modern data structures.

Data Modeling Mastery: Expertise in designing conceptual, logical, and physical data models for both structured (Relational/ERP) and unstructured (NoSQL/Lakehouse) datasets.

Modern Data Stack Strategy: Proficiency in designing hybrid-cloud data platforms (Snowflake, Databricks, or cloud-native AWS/Azure/GCP big data services) and ETL/ELT orchestration.

Data Governance Quality: Ability to implement enterprise-grade data governance frameworks (Data Cataloging, Lineage, Master Data Management) to ensure compliance with KVKK, GDPR, and holding-level audit requirements.

Security Privacy: Expert-level knowledge in data security, data privacy, and the technical integration of KVKK and GDPR regulations into the infrastructure.

Communication Technical Acumen: Possessing the high technical proficiency required to debate solutions with technical authorities, combined with the strong communication skills needed to guide and influence business units.

Leadership: A visionary with strong leadership qualities who doesn't just set the rules, but actively persuades stakeholders to ensure their successful implementation.

Continuous Learning: Always open to learning new technologies, tools, and architectural approaches.

Industry Experience: Preferably experienced in working with complex data structures within the Manufacturing (sector experience is a significant plus), Retail, or Telecommunications industries.

Education: Bachelor’s degree from an Engineering faculty.

Language Skills: Advanced level of both spoken and written English.

Domain Expertise: In-depth knowledge and profound experience in data architecture, data governance, and data strategy development.

Project Experience: Active, end-to-end involvement in Data Warehouse projects and the transition from traditional to modern data structures.

Data Modeling Mastery: Expertise in designing conceptual, logical, and physical data models for both structured (Relational/ERP) and unstructured (NoSQL/Lakehouse) datasets.

Modern Data Stack Strategy: Proficiency in designing hybrid-cloud data platforms (Snowflake, Databricks, or cloud-native AWS/Azure/GCP big data services) and ETL/ELT orchestration.

Data Governance Quality: Ability to implement enterprise-grade data governance frameworks (Data Cataloging, Lineage, Master Data Management) to ensure compliance with KVKK, GDPR, and holding-level audit requirements.

Security Privacy: Expert-level knowledge in data security, data privacy, and the technical integration of KVKK and GDPR regulations into the infrastructure.

Communication Technical Acumen: Possessing the high technical proficiency required to debate solutions with technical authorities, combined with the strong communication skills needed to guide and influence business units.

Leadership: A visionary with strong leadership qualities who doesn't just set the rules, but actively persuades stakeholders to ensure their successful implementation.

Continuous Learning: Always open to learning new technologies, tools, and architectural approaches.

Industry Experience: Preferably experienced in working with complex data structures within the Manufacturing (sector experience is a significant plus), Retail, or Telecommunications industries.

Education: Bachelor’s degree from an Engineering faculty.

Language Skills: Advanced level of both spoken and written English.