Data Architect

Strong commercial experience in data architecture and data engineering within complex environmentsHands-on expertise in modern data pipelines, integration frameworks, and distributed processingExperience with cloud-based data platforms and large-scale data ecosystemsGood understanding of batch, streaming, and event-driven architecturesExperience designing data models for both operational and analytical purposesKnowledge of APIs, interoperability standards, and system integration approachesExperience working with machine, telemetry, sensor, or operational data is highly valuedAbility to combine strategic architecture ownership with hands-on implementationStrong communication skills and confidence working with both technical and business stakeholdersNice to have:Experience in innovation-driven, industrial, infrastructure, engineering, or highly regulated environmentsFamiliarity with edge-to-cloud architectures, digital twins, simulation environments, or AI-ready platformsExposure to metadata management, governance, and lineage solutions We are looking for an experienced Data Architect / Engineering Lead to take ownership of designing and building modern data ecosystems supporting advanced technologies, AI initiatives, automation, and large-scale analytics. This is a strategic and hands-on role for someone who enjoys shaping architecture from the ground up while actively contributing to implementation.You will work in complex, multi-source environments, integrating data across enterprise systems, operational platforms, connected devices, external partners, and emerging technology solutions. The position requires balancing long-term architectural vision with practical delivery and execution. ,[Define and drive end-to-end data architecture, including ingestion, storage, transformation, integration, and consumption layers, Design and implement scalable data pipelines supporting structured, semi-structured, unstructured, and real-time data, Build integration patterns across enterprise applications, devices, APIs, operational systems, and third-party environments, Develop canonical data models enabling interoperability between fragmented systems and business domains, Support AI, analytics, automation, and data product initiatives through reliable and scalable data foundations, Establish engineering standards, DataOps practices, monitoring, and quality controls, Collaborate with security and governance teams to ensure data quality, lineage, access control, and compliance requirements, Lead technical decisions regarding platforms, architecture direction, and implementation strategy, Provide technical leadership while remaining actively involved in engineering delivery] Requirements: Data engineering, Data pipelines, Data models, System integration, edge-to-cloud architectures, digital twins, AI-ready platforms Additionally: Private healthcare, Sport subscription, Foreign languages classes, Life Insurance, Cafeteria system.