Data Architect

Avensys Consulting; Paris; Presencial; EUR 120.000 - 170.000; Jornada completa

Descripción de la vacante

Avensys Consulting Pte Ltd is seeking an AI Cloud Data Architect to design, implement, and govern enterprise-scale data and AI platforms on Azure. The role emphasizes cloud-native data architectures, AI/ML platforms, data governance, and modern analytics ecosystems.

We value 8–12+ years of experience, with 4–5 in cloud data architecture and 2–3 with GenAI/LLMs in real delivery. Experience in agentic AI and regulated industries is a strong differentiator.

Formación

• 8–12+ years overall experience, with at least 4–5 in cloud data architecture and a minimum of 2–3 years working with GenAI/LLMs/RAG in real delivery contexts.

Conocimientos

Cloud data architecture

GenAI/LLMs

AI governance

Data governance

Data lineage

Regulated industry

Azure Databricks

Azure Data Factory

Azure OpenAI

Python/PySpark

CI/CD

MCP Protocol

AI agents

Observability

Educación

Bachelor-level degree in a relevant field

Herramientas

Azure Databricks

Azure Data Factory

Azure OpenAI

OTEL

Descripción del empleo

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.

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.

Certifications worth flagging: Azure Solutions Architect Expert, Azure AI Engineer Associate, Databricks certifications — any of these are a positive signal.