> Markdown version of [/jobs/ext/4651146-data-engineer](https://www.wearedevelopers.com/jobs/ext/4651146-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Kyndryl - **Location:** Madrid, Spain (Remote available) - **Salary:** €71,233 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Mining, IBM DB2, Github, Graph Database, Integrated Development Environments, PostgreSQL, Microsoft Visual Studio, MongoDB, NoSQL, Cloudera, Software Engineering, Pinecone, Google Cloud, Data Storage Technologies, Cloud Platform System, Azure Data Factory, Retrieval-Augmented Generation, Vector Embeddings, Generative AI, Data Layers, Information Technology, Data Analytics, Weaviate, Milvus, Data Management, Cloud Migration, Data Pipelines, Databricks - **Published:** November 10, 2026 - **Apply:** https://dejobs.org/x/x/BC4AC80F61BE45FC873F5BC3900616FD/job/ ## About the Role *Expertise in data mining, data storage and Extract-Transform-Load (ETL) processes *Experience in data pipelines development and tooling, e.g., Glue, Databricks, Synapse, or Dataproc *Experience with both relational and NoSQL databases, PostgreSQL, DB2, MongoDB *Excellent problem-solving, analytical, and critical thinking skills *Ability to manage multiple projects simultaneously, while maintaining a high level of attention to detail *Ability to communicate with both technical and non-technical colleagues, to derive and translate technical requirements from business needs, *Experience working as a Data Engineer and/or in cloud modernization *Experience in Data Modelling, to create conceptual model of how data is connected and how it will be used in business processes *Professional certification, e.g. Open Certified Technical Specialist with Data Engineering Specialization *Cloud platform certification, e.g. AWS Certified Data Analytics - Specialty, Elastic Certified Engineer, Google Cloud Professional Data Engineer, or Microsoft Certified: Azure Data Engineer Associate *Understanding of social coding and Integrated Development Environments, e.g. GitHub and Visual Studio *Degree in a scientific discipline, such as Computer Science, Software Engineering, or Information Technology ## Description Your role As an AI Data Engineer at Kyndryl, you'll be the architect behind the high-performance data infrastructure that powers our autonomous systems. We aren't just moving tables; we're building the real-time pipelines that allow agentic AI to reason over the world's most complex enterprise environments. Join Kyndryl and gain the trust, autonomy and teamwork to help define what comes next. What you will do Orchestrate Intelligence *Architect for RAG: You'll design and scale the pipelines for Retrieval-Augmented Generation (RAG), transforming massive volumes of unstructured IT logs and documentation into optimized Vector Embeddings. *Scale vector infrastructure: You will be responsible for the health and performance of our vector databases (e.g., Pinecone, Milvus, or Weaviate), ensuring sub-second retrieval speeds for agentic reasoning loops. Master Data Transformation *Engineer semantic layers: Move beyond simple ETL to build knowledge graphs and semantic layers that provide agents with the necessary context to navigate complex infrastructure puzzles. *Automate data excellence: Using a keen eye for detail, you'll build automated data guardrails to detect noise, bias, or PII (Personally Identifiable Information) before it reaches the model, ensuring our AI remains safe and impactful. Own the Backbone *Solve meaningful challenges: Serve as the bridge between raw, messy data sources and deep technical AI work, identifying and resolving quality issues at the source. *Progress to production: With a well-defined methodology and software engineering prowess, you will build, deploy, and maintain the CI/CD pipelines for our data infrastructure, ensuring that our context window remains fresh and reliable.