Data Engineer

Why Keyrus, Why Now

we operationalize intelligence.

Intelligent systems are only as dependable as the data beneath them. Reliable pipelines, trusted data products, governed access, and meaningful enterprise context allow analytics, applications, models, and agents to operate consistently in production.

Keyrus

is an international group of 3000 experts across 30 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does. For more than 30 years, we have been building the data foundations that make intelligent systems work — designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalize intelligence. Intelligent systems are only as dependable as the data beneath them. Reliable pipelines, trusted data products, governed access, and meaningful enterprise context allow analytics, applications, models, and agents to operate consistently in production. Technology amplifies. Keyrus culture differentiates.

What You'll Architect

As a Data Engineer - AI & Data Platforms, you will design, build, and operate reliable data products and pipelines that support analytics, applications, and intelligent systems.

This is fundamentally a Data Engineering role, with an additional focus on preparing enterprise data for modern AI use cases. You will work across structured and unstructured information, transforming source data into trusted, reusable data products that can support analytics and operational applications as well as RAG, retrieval, evaluation, and agent-enabled systems.

Your Responsibilities

• Design, build, test, and operate production data pipelines and data products, moving data reliably from enterprise source systems into reusable consumption layers.
• Develop and maintain data models and transformation logic that preserve clear definitions, business context, and reliable use across analytical, operational, and AI workloads.
• Implement data quality, lineage, metadata, and observability controls, ensuring critical data is trustworthy, understandable, and monitored in production.
• Prepare structured and unstructured enterprise data for intelligent systems, including retrieval, grounding, RAG, semantic context, and evaluation datasets where required.
• Engineer reliable ingestion and processing patterns across APIs, databases, files, documents, events, and other relevant enterprise sources.
• Operate and improve data workloads in a major cloud/data ecosystem, balancing reliability, scalability, security, performance, and cost.
• Collaborate with AI Engineers and Software Engineers on stable data interfaces, without taking ownership of model behavior, agents, or the surrounding application.
• Document and reuse data‑engineering patterns, including data contracts, transformations, controls, dependencies, and operational knowledge that can strengthen future Keyrus engagements.

Who You Are

You are a hands-on Data Engineer who understands that moving data is not enough. Data must remain trusted, understandable, available, and usable once a system is operating in production.

You think from source to consumption and can determine what is required to turn fragmented enterprise data into reliable data products.

You combine strong engineering fundamentals with practical judgment. You understand that analytics, applications, RAG systems, evaluations, and agents may consume data differently, and you design the foundation accordingly without trying to own the intelligence or application layers yourself.

What You Bring

Must Have

• Strong hands-on experience designing, building, and operating production data pipelines and data products.
• Strong SQL and data-modeling fundamentals, with the ability to design clear, maintainable transformation and consumption layers.
• Practical engineering capability in Python or another language commonly used for production data engineering.
• Deep practical expertise in at least one major cloud data ecosystem, with the ability to apply the same engineering principles across comparable technologies.
• Experience implementing data quality, testing, monitoring, lineage, or observability within production data workflows.
• Understanding of production engineering concerns including reliability, scalability, security, access, maintainability, and performance.

Should Have

• Experience preparing structured and unstructured data for AI or GenAI use cases.
• Practical understanding of how RAG, retrieval, embeddings, semantic context, and evaluation datasets depend on enterprise data.
• Experience working with distributed processing, orchestration, and modern data-transformation approaches.
• Experience with batch inge