Data Architect (m/k)

Technologies we use

About the project

Your responsibilities

• Define and evolve the end-to-end data platform architecture, including ingestion, lakehouse/warehouse, transformation, orchestration, governance, observability, and AI enablement.
• Establish a unified, standardised data model supporting operational, customer, and supplier workflows.
• Lead architectural decisions around storage, table formats, batch and streaming patterns.
• Design and implement scalable ELT/ETL pipelines across enterprise systems, network telemetry, and external partners.
• Define standards for raw, refined, and curated data layers, ensuring data quality, lineage, and reprocessability.
• Introduce CDC, schema evolution, and automated ingestion patterns.
• Architect the data foundations for AI and ML, including feature stores, model lifecycle, and ML observability.
• Enable AI-driven use cases such as pricing intelligence, prediction, automation triggers, and real-time insights.
• Support semantic and conversational data access for intelligent workflows.
• Design scalable data governance and metadata architectures, including catalogues, lineage, and ownership.
• Implement automated data quality checks and observability embedded in pipelines.
• Act as the technical authority for data architecture decisions across teams.
• Guide data engineers, ML engineers, and analysts through standards and best practices.
• Partner with platform, product, and operations teams to make data a first-class capability.

Employer requirements

• Strong hands-on experience building modern cloud-native data platforms (AWS preferred).
• Expertise in lakehouse or warehouse architectures (e.g. Snowflake, Redshift, Databricks) and modern table formats.
• Experience with ELT/ETL tooling, orchestration frameworks, and streaming data.
• Solid understanding of MLOps and AI data architectures.
• Strong SQL and Python skills; comfortable with DevOps and everything-as-code practices.
• Proven ability to design scalable, modular architectures supporting real-time workflows and automation.
• Experience standardising data across heterogeneous systems and event-driven environments.
• Strong communication skills with the ability to influence technical and non-technical stakeholders.
• Hands-on builder with strong ownership.
• Automation-first, code-first approach.
• Comfortable designing data platforms for AI-native and real-time products.
• Not a BI or dashboard-focused role.

Work organization

Work style

What employer offers

• International, collaborative work environment.
• Exposure to large-scale cloud, data, and AI platforms.
• Strong learning and development support.
• Competitive compensation and benefits.
• Hybrid work.