Data Architect
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We are looking for an experienced AI Cloud Data Architect to design, implement, and govern enterprise-scale data and AI platforms on cloud environments. The ideal candidate will have strong expertise in cloud-native data architectures, AI/ML platforms, data governance, and modern analytics ecosystems. This role will drive the design of scalable, secure, and resilient solutions that enable advanced analytics, Generative AI, and machine learning use cases.
La description complète du poste couvre toutes les compétences associées, l'expérience antérieure et les qualifications que les candidats sont censés avoir.
Experience: 8–12+ years overall, with at least 4–5 of those in cloud data architecture (not just engineering) and a minimum of 2–3 years working with GenAI/LLMs/RAG in a real delivery context — not just personal projects. Candidates with experience in agentic AI — multi-agent orchestration, AI agent design, or agentic platform delivery — are strongly preferred. At least one regulated industry engagement at architect level is required. Total years matter less than the right combination of data architecture seniority and genuine GenAI/agentic AI depth.
Industry: Banking or Financial Services strongly preferred. Regulated industries (insurance, public sector, telco) acceptable.
Cloud: Azure-native candidates first. AWS or GCP experience is fine as a secondary, but they must know Azure well — the client stack is Azure-centric.
Data Architecture is not enough on its own: A pure data warehouse, BI, or ETL architect won't fit. Look for candidates who have crossed into GenAI territory — RAG, LLMs, vector databases, AI agents. This is non-negotiable for the role.
The governance angle is critical: This is a data governance intelligence platform, not a data platform build. Prioritise candidates who have worked on metadata management, data lineage, compliance frameworks, or audit infrastructure — not just pipeline engineering.
Agentic AI experience is a differentiator: Candidates who have worked with agent orchestration, MCP (Model Context Protocol), human-in-the-loop workflows, or guardrail/observability design for AI systems will stand out strongly.
Core tech to look for: Azure Databricks, Azure Data Factory, Azure OpenAI, Entra ID, RAG/LLM, Python/PySpark, OTEL or audit logging, CI/CD. xuezdbg
Certifications worth flagging: Azure Solutions Architect Expert, Azure AI Engineer Associate, Databricks certifications — any of these are a positive signal.
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