Engineering Data Architect

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MidHybryda·ŁódźFull-time

#475280·Dodano 2 dni temu·0

Źródło: nofluffjobs.com

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Tech Stack / Keywords

Data scienceEnterprise ArchitectData engineeringData modelsData LakeData warehousesPLMERPCloudData managementSecurityAIMachine LearningStakeholder management

Podsumowanie oferty

Define and evolve the data architecture foundation for global engineering platforms, digital engineering solutions, analytics, and AI initiatives. Translate business and engineering needs into data models, integrations, governed datasets, and AI-ready data products across engineering and software delivery teams.

Wymagania

Master’s degree in Computer Science, Information Systems, Data Science, Software Engineering, Engineering, or a related discipline.
5+ years of experience in data architecture, information architecture, enterprise architecture, solution architecture, data engineering, or related technology roles.
Experience designing large data models, integration architectures, data platforms, data lakes, data warehouses, or data products in complex global organizations.
Solid understanding of PLM, ERP, engineering platforms, software delivery ecosystems, cloud data platforms, analytics, and enterprise integration technologies.
Practical experience with data governance, metadata management, master data management, data quality, security, privacy, compliance, and lifecycle management.
Experience supporting AI, Machine Learning, Generative AI, semantic search, knowledge management, or advanced analytics initiatives with governed and trusted data foundations.
Excellent stakeholder management, facilitation, communication, systems thinking, and architecture leadership skills.

Obowiązki

Define and maintain the data architecture roadmap for Engineering Platforms, aligning it with enterprise architecture, digital transformation, and data strategy principles.
Design canonical, reusable data models for products, processes, systems, and digital twins.
Establish integration patterns, APIs, event models, data exchange mechanisms, and data services connecting engineering applications, cloud platforms, AI services, analytics platforms, and digital engineering solutions.
Create a trusted data foundation for engineering analytics, executive reporting, platform observability, software delivery performance measurement, and value tracking.
Define governance, ownership, stewardship, metadata, reference data, lineage, quality, privacy, security, and compliance standards for engineering and platform data.
Enable AI and Generative AI use cases by defining AI-ready data products, semantic models, retrieval-augmented generation patterns, knowledge graphs, vector search foundations, training datasets, and governed data access models.
Partner with platform owners, solution architects, product managers, business analysts, engineering leaders, and Data Analytics & AI teams to translate business requirements into scalable, secure, maintainable data solutions.
Lead data architecture reviews for projects, demands, business cases, and change requests to ensure consistency, interoperability, reuse, compliance, and long-term platform sustainability.
Drive continuous improvement of data quality, reporting automation, self-service analytics, and engineering intelligence across global engineering organizations.
Develop architecture standards, blueprints, decision records, reference architectures, and implementation guidance for data platforms, integrations, analytics, and AI-enabled engineering solutions.

Benefity

Private medical care.
Life insurance.
Access to fitness and wellness programs.
Access to a benefits platform with discounts and perks.
Employee Capital Plans (PPK).
Equipment for working from home or allowances for setting up a workplace at home.
Allowance for spectacles and contact lenses.
Company events and team-building activities.
Psychological support program.
Free parking.

Opieka zdrowotna

Ubezpieczenie

Karta sportowa

Dodatkowe benefity

Spotkania integracyjne

Parking dla aut

Hitachi Energy

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