Data Engineer

h3Why Keyrus, Why Now /h3pwe operationalize intelligence. /ppIntelligent 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. /ph3Keyrus /h3pis 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. /ph3What You'll Architect /h3pAs a Data Engineer
- AI Data Platforms, you will design, build, and operate reliable data products and pipelines that support analytics, applications, and intelligent systems. /ppThis 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. /ph3Your Responsibilities /h3ulliDesign, build, test, and operate production data pipelines and data products, moving data reliably from enterprise source systems into reusable consumption layers. /liliDevelop and maintain data models and transformation logic that preserve clear definitions, business context, and reliable use across analytical, operational, and AI workloads. /liliImplement data quality, lineage, metadata, and observability controls, ensuring critical data is trustworthy, understandable, and monitored in production. /liliPrepare structured and unstructured enterprise data for intelligent systems, including retrieval, grounding, RAG, semantic context, and evaluation datasets where required. /liliEngineer reliable ingestion and processing patterns across APIs, databases, files, documents, events, and other relevant enterprise sources. /liliOperate and improve data workloads in a major cloud/data ecosystem, balancing reliability, scalability, security, performance, and cost. /liliCollaborate with AI Engineers and Software Engineers on stable data interfaces, without taking ownership of model behavior, agents, or the surrounding application. /liliDocument and reuse data‑engineering patterns, including data contracts, transformations, controls, dependencies, and operational knowledge that can strengthen future Keyrus engagements. /li /ulh3Who You Are /h3pYou 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. /ppYou think from source to consumption and can determine what is required to turn fragmented enterprise data into reliable data products. /ppYou 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. /ph3What You Bring /h3h3Must Have /h3ulliStrong hands-on experience designing, building, and operating production data pipelines and data products. /liliStrong SQL and data-modeling fundamentals, with the ability to design clear, maintainable transformation and consumption layers. /liliPractical engineering capability in Python or another language commonly used for production data engineering. /liliDeep practical expertise in at least one major cloud data ecosystem, with the ability to apply the same engineering principles across comparable technologies. /liliExperience implementing data quality, testing, monitoring, lineage, or observability within production data workflows. /liliUnderstanding of production engineering concerns including reliability, scalability, security, access, maintainability, and performance. /li /ulh3Should Have /h3ulliExperience preparing structured and unstructured data for AI or GenAI use cases. /liliPractical understanding of how RAG, retrieval, embeddings, semantic context, and evaluation datasets depend on enterprise data. /liliExperience working with distributed processing, orchestration, and modern data-transformation approaches. /liliExperience with batch ingestion and one or more additional patterns such as APIs, change data capture, event-driven processing, or strea