Role & responsibilities :

- Define data strategy + target-state architecture, including roadmap and reference patterns.

- Design and govern enterprise data architecture (models, integration patterns, metadata/lineage).

- Architect modern data platforms (cloud/hybrid lakehouse/warehouse, batch + streaming).

- Enable GenAI architectures (RAG, vector stores/embeddings, evaluation, safety guardrails).

- Establish data governance (quality, access controls, privacy, retention, stewardship).

- Partner with engineering/security/product to deliver AI-ready data products with SLAs and observability.

Required Qualifications / Skills :

- 7- 12+ years (adjust) in data architecture, data engineering, or platform architecture roles.

- Proven experience creating data strategy + architecture deliverables (target state, roadmap, governance, operating model).

- Deep knowledge of modern data patterns :

1. Dimensional and domain data modeling; data contracts; data products

2. Batch + streaming; CDC (change data capture); API and event integration

- Strong proficiency with common tech stacks (experience with several) :

1. Cloud data services (AWS/Azure/GCP), data lakehouse/warehouse

2. Orchestration, ETL/ELT, SQL, and at least one programming language (Python/Scala/Java)

- AI familiarity with practical application to data platforms :

1. Feature engineering concepts, ML pipelines, evaluation/monitoring basics

2. GenAI concepts : embeddings, vector databases, RAG, prompt orchestration, safety/guardrails

- Strong understanding of security and risk for data/AI : IAM, encryption, PII handling, access controls, auditability.

Nice-to-Have :

- Experience with Data Mesh or domain-based operating models.

- MDM (master data management), semantic layers, knowledge graphs.

- Hands-on with MLOps/LLMOps tooling and model governance processes.

- Experience in regulated environments (financial services, healthcare, public sector).

- Relevant certifications (cloud, data, security, AI)optional.

Soft Skills / Ways of Working :

- Can translate business objectives into architecture decisions and an executable backlog.

- Strong stakeholder management; comfortable presenting to senior leaders.

- Pragmatic, delivery-oriented : balances ideal architecture with timelines and constraints.

- Clear documentation habits and strong communication.

Deliverables Youll Own (Examples) :

- Data & AI target architecture + roadmap (1224 months)

- Data governance and standards (quality, metadata, lineage, access)

- Solution architecture for priority use cases (including RAG patterns where relevant)

- KPI framework for data reliability and AI readiness