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AI Data Architect (m/f/d)

Location:

Madrid, ESP

Company: BASF Digital Solutions S.L.

Job Field:
Digitalization

Job Type: Permanent

Flexible Work Options:
Hybrid

Welcome to BASF Digital Hub Madrid attracts, grows, and develops passionate people who will meaningfully impact the digital future of BASF. Come join us and be a part of our digitalization journey.

The AI & Automation Enablement team designs and engineers scalable AI and automation solutions that deliver measurable business value. By combining strong architecture, engineering excellence, and industrialization capabilities, we enable BASF to turn opportunities into production-ready systems – consistently, reliably, and at scale.

AI Data Architect in a project-oriented team who designs scalable, enterprise-grade data architectures and leads the engineering of robust data platforms, collaborating across domains and teams to enable the development and industrialization of AI solutions that deliver measurable business value.

• Define and own the end-to-end data architecture for AI solutions, from ingestion and storage to serving layers powering models and analytics (e.g., batch pipelines, real-time APIs).
• Design the data foundations for compound AI systems, including retrieval architectures (e.g. RAG), vector and knowledge stores, and feature/embedding layers.
• Establish data modelling standards, semantic layers, and reusable reference architectures across workloads (batch, streaming, real-time, analytical, AI-driven) across the organization.
• Define and enforce governance frameworks for data quality, lineage, security, and compliance, ensuring trustworthy and well-managed data products.
• Set architectural guidelines and best practices for scalable data platforms and pipelines, and ensure their consistent adoption.
• Provide technical leadership and guidance to data engineers, data scientists, and business partners on data and AI architecture.
• Evaluate emerging technologies and define the strategic roadmap for the data and AI platform.
• Stay current with trends and best practices in data architecture, AI, and cloud platforms.

Bachelor's degree or Master's in computer science, Information Systems, or a related field.

5+ years of experience in data engineering and data architecture, including designing data models, platforms, and end-to-end data pipelines.

Proven experience defining data architecture for AI/analytics solutions on a major cloud platform (Azure preferred; AWS a plus) and Big Data architectures.

Data architecture and modelling for relational and No

SQL systems, including modern storage paradigms (e.g., object storage, lakehouse, warehouse).

Design of scalable data platforms and architectural patterns (e.g., Lakehouse, Data Mesh, Medallion), including governance, lineage, and quality considerations.

Architecture of compound AI systems, including data layers for AI solutions (e.g., RAG, GraphRAG), vector stores, graph databases, and embedding pipelines.

Strong programming skills in Python, including asynchronous programming (asyncio) and data processing.

Apache Spark and Databricks platform

Cloud data platform expertise, preferably Azure Could and Azure data platform services.

Streaming and event-driven architectures, using streaming services such as RabbitMQ or Apache Kafka

Big data and large-scale compute architectures, including batch and real-time processing patterns.

Design of microservices and distributed systems, including API-based data services.

Containerization and deployment practices using Docker (and Kubernetes as a plus)

Data security, privacy, and compliance (e.g., GDPR), including encryption, access control, and data masking/anonymization

Software engineering practices, including Agile methodologies (Scrum, Kanban) and Dev Ops (CI/CD with Git Hub Actions)

Container orchestration platforms:
Kubernetes

Graph database technologies and data modelling

Workflow orchestration and task management frameworks such as Apache Airflow and Celery

AI/ML platforms and cloud AI services

LLM and agentic solution patterns, including prompt engineering and orchestration of AI agents.

MLOps practices, including model lifecycle…