Data engineer in supply chain

NTT DATA Europe & Latam; Barcelona; Hybride; EUR 60 000 - 90 000; Plein temps

Avantages offerts par ce poste

Гибридная работа

Медицинская страховка

Корпоративный университет и обучение

Résumé du poste

NTT DATA Europe & Latam объявляет набор инженера по данным для работы в Digital Supply Chain & Innovation (DSI). В задачу входит разработка продвинутых дата-решений, интеграция данных и создание инфраструктуры для генеративного ИИ и агентов в цепочках поставок и производстве.

Фокус на объединении Data Engineering, Cloud, AI Engineering и IT/OT архитектуры, а также создание масштабируемых, безопасных и управляемых платформ на Lakehouse и облаке.

Qualifications

• Опыт разработки пайплайнов и обработки данных.
• Знание архитектур Data Lakehouse и хранилищ.
• Опыт работы с облачными технологиями и CI/CD.

Responsabilités

• Разработка и поддержка ingestion, трансформации и обработки данных (батч и near-real-time).
• Интеграция данных из ERP, MES, WMS, TMS, датчиков и API.
• Создание Data Products и контрактов, обеспечение качества и наблюдаемости.

Connaissances

SQL

Python

ETL/ELT

Spark

Data Modeling

Cloud

CI/CD

Git

APIs

Data Lakehouse

Outils

Delta Lake

Parquet

Description du poste

Описание

We are looking to add a Data Engineer to NTT DATA's Digital Supply Chain & Innovation (DSI), with an interest in developing advanced data, Generative AI and agentic system solutions applied to Supply Chain and Manufacturing. The role will join multidisciplinary teams designing, building and evolving AI solutions for enterprise and industrial environments, working at the intersection of Data Engineering, Cloud, AI Engineering and IT/OT architecture. We are not looking only for someone focused on building data pipelines, but an engineer able to understand how data must be captured, contextualized, governed and served to become the foundation for copilots, AI agents, decision systems and intelligent solutions deployed across real business processes.

Задачи

Build and maintain data ingestion, transformation and processing pipelines, both batch and near-real-time or event-driven. Integrate information from multiple enterprise and industrial systems: ERP, MES, WMS, TMS, APS, databases, APIs, IoT, OT platforms and document sources. Build data layers that allow LLMs and AI agents to access corporate information in a controlled way, including Retrieval-Augmented Generation (RAG), hybrid search and structured retrieval patterns. Design data integration mechanisms for agentic architectures through APIs, events, tools, services and integration protocols such as MCP, where applicable. Participate in the design and implementation of Data Products and Data Contracts, establishing schemas, quality, ownership, SLAs, versioning and observability mechanisms. Implement pipelines for structured, semi-structured and unstructured data, including chunking, embeddings, indexing and metadata enrichment strategies. Design and operate solutions on Lakehouse and Cloud Data Platform architectures, applying scalability, resilience, security and efficiency principles. Integrate cloud solutions with industrial environments and IT/OT architectures, including APIs, events, IoT and patterns based on Unified Namespace (UNS). Collaborate with architecture, AI Engineering and Data Science professionals, and business specialists, to turn Supply Chain and Manufacturing needs into technically viable solutions. Participate in building reusable accelerators, connectors, components and patterns that can be used across different clients, plants and use cases. Define and implement Data Quality, observability and monitoring mechanisms to identify problems with data completeness, consistency, freshness or availability. Apply CI/CD, Infrastructure as Code, testing and automation practices to the lifecycle of data platforms and products. Participate in PoCs and technology experimentation, evaluating new technologies and determining their viability, performance and scalability before industrialization. Contribute to the evolution of DSI reference architectures for GenAI, Agentic AI, Supply Chain and Manufacturing solutions.

Требования

Ability to design solutions, not only implement closed specifications. Good knowledge of SQL and relational and analytical data modeling. Experience programming mainly in Python and developing data processing components. Experience building ETL/ELT processes and integration pipelines. Knowledge of modern data architectures: Data Lake, Lakehouse, Data Warehouse and medallion architectures. Experience with distributed processing technologies such as Spark. Knowledge of data storage formats and technologies such as Parquet and Delta Lake. Experience working with relational databases and ability to understand different persistence patterns. Knowledge of REST APIs, system integration and service-oriented architectures. Familiarity with cloud technologies and with the security, identity, networking and deployment principles required to operate an enterprise data platform. Ability to design solutions with performance, scalability, resilience, maintainability and cost in mind. Good development practices: Git, testing, CI/CD, documentation and code review. Ability to work jointly with architects, Data Scientists, AI Engineers and functional specialists. Technological curiosity and ability to learn emerging technologies quickly. Interest in understanding the business process behind the data and not limiting oneself exclusively to its technological dimension. We especially value profiles able to move naturally between software, data and architecture, avoiding an excessively narrow view of Data Engineering.

Условия

Permanent contract from day one. Compensation in line with your experience and knowledge. Personalized career plan and category and salary reviews. Flexible benefits, to allocate part of your salary to social benefits (restaurant voucher, transport voucher, childcare), resulting in tax savings, plus a flexible allowance in addition to your salary for remote work and meals. Medical insurance + life and accident insurance. Hybrid work model and full-time schedule (Monday to Thursday from 8:30 a.m. to 6 p.m. and Friday from 8 a.m. to 3 p.m.). 23 vacation days + 2 personal leave days + 2 company holidays: December 24 and 31. Free access to courses at our corporate university (languages, technical training and competency skills). A diverse, creative, collaborative and multinational work environment.