Data Architect

Experis to światowy lider rekrutacji specjalistów i kadry zarządzającej w kluczowych obszarach IT. Z nami znajdziesz konkurencyjne oferty zatrudnienia oraz ciekawe projekty IT skierowane zarówno do ekspertów z wieloletnim doświadczeniem, jak i osób, które dopiero zaczynają swoją przygodę w branży IT.

• Proven experience in data architecture and data engineering within complex, multi-source environments
• Strong hands-on experience building modern data pipelines and integration frameworks
• Experience with cloud-based data platforms and distributed data processing systems
• Solid understanding of batch, streaming, and event-driven architectures
• Experience with data modeling for both operational and analytical use cases
• Experience integrating machine, device, telemetry, or operational data into enterprise data platforms
• Strong knowledge of APIs, interoperability, and system integration patterns
• Ability to work as both an architect and a hands-on delivery lead
• Excellent communication skills with both technical and business stakeholders

Nice to have:

• Experience in frontier technology, industrial, infrastructure, or innovation-driven environments
• Familiarity with edge-to-cloud architectures, digital twins, simulation data, or AI-ready platform design
• Experience with metadata management, data lineage, and governance tooling
• Background in regulated, operationally sensitive, or multi-party ecosystems

• Define and own the end-to-end data architecture across source systems, ingestion, streaming pipelines, storage, transformation, APIs, and consumption layers
• Design and implement scalable data pipelines for structured, semi-structured, unstructured, and real-time data
• Integrate data from enterprise systems, connected devices, sensors, operational platforms, third-party providers, and partner ecosystems
• Establish canonical data models and integration patterns across key entities (e.g. assets, events, locations, products, devices, users, operational processes)
• Collaborate with security and governance stakeholders to embed access control, lineage, quality, and compliance into the architecture
• Support real-time and near-real-time ingestion and processing use cases
• Drive interoperability across fragmented technical and business environments
• Define engineering standards, DataOps practices, monitoring, and reliability controls
• Enable downstream analytics, AI, automation, simulation, and data product consumption
• Provide technical leadership across platform selection, implementation sequencing, and delivery trade-offs