Key Responsibilities :

- Design and implement enterprise-scale data architectures using Data Vault 2.0 methodologies.

- Define and govern data modeling standards across the organization.

- Architect and optimize Data Vault components including : 1. Hubs 2. Links 3. Satellites 4. Hash Keys 5. PIT Tables 6. Bridge Tables.

- Drive cloud-based data platform architecture and modernization initiatives.

- Guide engineering teams on Data Vault best practices and implementation strategies.

- Leverage AI-assisted development tools such as GenAI, LLMs, and Copilot to accelerate data engineering and modeling workflows.

- Collaborate with data engineers, analytics teams, and business stakeholders to deliver scalable data solutions.

- Design data architectures that support analytics, reporting, AI, and machine learning initiatives.

- Establish governance, scalability, performance, and data quality standards.

- Evaluate emerging technologies and continuously improve enterprise data architecture capabilities.

Required Skills :

- 10+ years of overall IT/Data experience.

- 5+ years of experience in Enterprise Data Architecture and Data Modeling.

- Deep expertise in : 1. Data Vault 2.0 2. Enterprise Data Modeling 3. Data Architecture.

- Strong experience with : 1. Snowflake 2. Azure Synapse 3. Cloud Data Platforms.

- Advanced SQL proficiency.

- Experience implementing AI-assisted development tools including : 1. GenAI 2. LLMs 3. GitHub Copilot.

- Strong understanding of scalable and modern data architecture patterns.

Preferred Skills :

- Certified Data Vault 2.0 Practitioner (CDVP2).

- Experience with : 1. dbt 2. Automated Data Transformations 3. dbt Macros.

- Understanding of : 1. Data Mesh 2. Modern Data Platforms 3. Distributed Data Architectures.

- Experience supporting AI and Machine Learning data ecosystems.

Behavioral Competencies :

- Ability to simplify and manage complex data ecosystems.

- Strong stakeholder management and communication skills.

- Continuous learning mindset with passion for emerging technologies.

- Ability to drive clarity, collaboration, and innovation across teams.

- Strong decision-making and problem-solving capabilities.

Education :

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