Data Engineer - AI & Data Platforms

Keyrus; Grimbergen; On-site; EUR 70,000 - 110,000; Full time

Benefits offered by this job

Mobility budget

Car with fuel or charging

Daily net allowances

Monthly net allowances

Eco vouchers

Hospitalisation insurance

Group insurance

Bonus scheme

Job summary

Keyrus is seeking a hands-on Data Engineer - AI & Data Platforms to design, build, and operate reliable data products and pipelines. You will prepare enterprise data for modern AI use cases and work across structured and unstructured information to deliver reusable data assets that support analytics, applications, and RAG-enabled systems.

You will collaborate with AI and Software Engineers to ensure stable data interfaces, maintain data quality, and implement observability in production.

Qualifications

• Strong hands-on experience designing, building, and operating production data pipelines and data products.
• Strong SQL and data-modeling fundamentals, with clear transformation and consumption layer design.
• Proficiency in Python or another language commonly used for production data engineering.
• Deep expertise in at least one major cloud data ecosystem (e.g., Databricks, Snowflake, BigQuery).
• Experience implementing data quality, testing, monitoring, lineage, or observability within production data workflows.

Responsibilities

• Design, build, test, and operate production data pipelines and data products moving data into reusable consumption layers.
• Develop and maintain data models and transformations with clear business context.
• Implement data quality, lineage, metadata, and observability controls in production.
• Prepare structured and unstructured data for AI use cases, including retrieval and grounding datasets.
• Collaborate with AI and Software Engineers on stable data interfaces and governance.

Skills

SQL & data modeling

Python

Cloud data ecosystems

Data quality & observability

Data pipelines

dbt / data tooling

Tools

Databricks

Snowflake

Airflow

Kubernetes

Job description

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 ingestion and one or more additional patterns such as APIs, change data capture, event-driven processing, or streaming.
• Working knowledge of CI/CD, automated testing, version control, and repeatable deployment practices for data solutions.
• Experience with vector or hybrid search infrastructure used for retrieval-based AI systems/ Feeding and maintaining RAG indexes
• Rapid prototyping, from mockup to working proof of concept in days (Streamlit)
• Experience collaborating with AI, software, analytics, architecture, or governance teams that consume or define requirements for the data products you build.

Could Have

• Experience with semantic layers, knowledge graphs, ontologies, or enterprise knowledge architectures.
• Experience building data foundations for agentic systems or complex GenAI applications.
• Experience with real-time or high-volume streaming architectures.
• Experience with ML data pipelines, feature stores, evaluation datasets, or MLOps integration
• Dashboards when needed, BI restitution on top of the data products
• Experience in regulated, high-security, or data‑sovereignty‑sensitive environments.
• Consulting or professional‑services experience.

Illustrative Technology Environments - Not All Required

Depending on the engagement, Keyrus data environments may include technologies such as Databricks, Snowflake, Microsoft Fabric, BigQuery, Redshift, Spark, dbt, Airflow, or comparable cloud‑native data services and orchestration technologies. The role does not require expertise across all of these platforms. Candidates should demonstrate strong depth in at least one relevant ecosystem and the engineering judgment to work across comparable environments.

What Makes You Successful

• You think in terms of trusted data products, not pipelines as an end in themselves.
• You combine strong engineering fundamentals with production discipline.
• You identify data-quality and reliability risks before they become downstream AI or application failures.
• You know when a requirement belongs to Data Engineering and when it should move to AI, Software, Architecture, or Governance.
• You make pragmatic decisions about performance, scalability, maintainability, and cost.
• You document what you build and turn successful data patterns into reusable knowledge across engagements.

Rewards - What We Offer At Keyrus

Growth

At Keyrus we have created an environment where people are supported, challenged and connected to the people around them. Working on impactful Data & Digital projects you will develop a broad and versatile skill set and stay ahead in a fast‑evolving, AI‑driven landscape. Our digital learning platform, with over 30,000 courses including in‑house trainings developed by our experts, is a perfect way to stay on top of your game. A generous training budget is available to help foster a culture where learning, feedback and continuous improvement is the norm. You will have a dedicated coach, benefit from regular check‑ins, learn via different communities and have clear alignment with your team and company goals. We want you to grow with confidence. We therefore place a strong emphasis on ownership, open feedback and continuous development. When people feel trusted, connected and inspired, they do their best work!

Rewards

Mobility budget? Car with fuel or charging car? We offer both so you can choose! On top of that we also have daily and monthly net allowances, eco vouchers, hospitalisation (DKV) insurance and group insurance, as well as a generous bonus scheme that you can use on our flexible reward plan.

What We Stand For

Collective Intelligence — We combine expertise, functions, and geographies to solve complex client challenges.

Reliability — We deliver complex work with rigor and build lasting client trust.

Pragmatism — We prioritize concrete impact and measurable value over abstract technological discourse.

Entrepreneurial Spirit — Curiosity, energy, and freedom enable initiative and sustained innovation.

Our Hiring Process

We keep our process straightforward and respect candidates' time. The local process should normally include:

• A conversation with the Talent team to discuss the candidate's background, expectations, and what Keyrus can offer.
• A technical architecture conversation focused on recent production AI architecture, architecture decisions, system boundaries, non‑functional requirements, and the candidate's primary technology ecosystem.
• An architecture case or representative challenge to understand how the candidate evaluates trade‑offs, defines a target architecture, and communicates decisions to engineering and client stakeholders, followed by a final conversation with the relevant technical or business leadership. The assessment should focus on architectural proficiency and decision‑making rather than the number of technologies a candidate can list.

About Keyrus

At Keyrus, we help organizations move from experimental to industrialized AI, from isolated agents to orchestrated systems, and from insight to execution. As Architects of Intelligence, we design the Operating System of the intelligent enterprise, embedding intelligence into business processes so that it creates sustainable, measurable value. We operationalize intelligence. Powered by our proprietary Human Orchestrated Model™ (HOM), we architect reliable:

• Intelligence Foundations
• Human in Command governance
• Performance Steering

With 30+ years of expertise and 3000 experts across 30 countries, we build adaptive, resilient intelligent environments where technology amplifies human capability and performance compounds over time. AI does not transform businesses. Architected intelligence does. Keyrus is listed on Euronext Growth Paris. (ALKEY - ISIN Code: FR0004029411 - Reuters: KEYR.PA - Bloomberg: ALKEY: FP). For more information: www.keyrus.com For over 30 years, Keyrus have been Architects of intelligent systems. We have combined engineering, business consulting, data architecture, and AI in real operational environments. We have done this whilst never forgetting a more human enterprise of allowing humans to design, supervise, and shape what only they can create.. Find out more on our services on keyrus.com.