Data Architect (all genders)

Mettler Toledo AG; Warszawa; On-site; PLN 60,000 - 85,000; Full time

Job summary

Mettler Toledo AG in Warsaw, Poland is looking for professionals to define and govern scalable enterprise data platform architecture. The role includes establishing data ingestion standards, ensuring compliance with security regulations, and architecting for performance and scalability.

The ideal candidate will collaborate with engineering and data science teams, ensure adherence to governance frameworks, and provide architectural guidance on data topics. The position offers opportunities to work on innovative data solutions in a dynamic environment.

Responsibilities

• Define and govern scalable enterprise data platform architecture across lakehouse, warehouse, real-time, and batch workloads for analytics.
• Establish standards for data ingestion, distribution, governance, and management.
• Ensure compliance with regulatory, security, and enterprise standards.

Job description

Responsibilities

• Define and govern scalable enterprise data platform architecture across lakehouse, warehouse, real-time and batch workloads for analytics, AI/ML, self-service and data products.


• Establish standards for data ingestion, distribution, governance, and management.


• Define architecture principles for storage, processing, access, and integration (APIs, BI, semantic layers).


• Ensure support for batch, streaming, and event-driven workloads across structured and unstructured data sources (SAP, business applications, IoT).


• Design reusable platform services and components to accelerate delivery across domains.


• Implement enterprise data governance framework (metadata, catalog, lineage, ownership, quality, policies).


• Ensure security-by-design (classification, RBAC/ABAC, encryption, data protection).


• Enforce data quality frameworks across applications and pipelines.


• Ensure compliance with regulatory, security, and enterprise standards.


• Architect for performance, scalability, cost-efficiency, and high availability.


• Define monitoring, observability, alerting, and SLA/SLO-driven reliability standards.


• Define best practices for DataOps, CI/CD, IaC, and platform lifecycle management.


• Collaborate with engineering, data science, BI, and architecture teams.


• Enable self-service analytics, governed consumption, and data product development.


• Provide architectural guidance and act as technical authority on data topics.


• Ensure adherence to security, compliance, and data protection standards.