Data Engineer - AI & Data Platforms

Location: Grimbergen, Belgium; Brussels · Hybrid

Company

hirify.global is an international data, digital, and AI consulting group with 3,000 experts across 30 countries, helping organizations build and operationalize intelligent systems.

What you will do

• Design, build, test, and operate production data pipelines and reusable data products across enterprise source systems and consumption layers.
• Develop data models and transformation logic with clear definitions, business context, and reliable use across analytical, operational, and AI workloads.
• Implement data quality, testing, lineage, metadata, monitoring, and observability controls for production data workflows.
• Prepare structured and unstructured enterprise data for retrieval, grounding, RAG, semantic context, and evaluation datasets.
• Engineer ingestion and processing across APIs, databases, files, documents, events, batch, and other enterprise sources.
• Collaborate with AI and Software Engineers on stable data interfaces, and document reusable engineering patterns and operational knowledge.

Requirements

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

Nice to have

• Experience preparing structured and unstructured data for AI or GenAI, including RAG, retrieval, embeddings, semantic context, evaluation datasets, and RAG indexes.
• Experience with distributed processing, orchestration, data transformation, batch ingestion, APIs, change data capture, event-driven processing, or streaming.
• Working knowledge of CI/CD, automated testing, version control, and repeatable deployment practices.
• Rapid prototyping from mockup to working proof of concept in days using Streamlit.
• Experience with semantic layers, knowledge graphs, ontologies, agentic systems, ML data pipelines, feature stores, MLOps, dashboards, or BI.
• Experience in regulated or high-security environments, data-sovereignty-sensitive settings, consulting, or professional services.
• Illustrative environments include Databricks, Snowflake, Microsoft Fabric, BigQuery, Redshift, Spark, dbt, Airflow, and comparable cloud-native services; expertise in all platforms is not required.

Culture & Benefits

• Work on data and digital projects with opportunities for continuous development and a broad, versatile skill set.
• Access to more than 30,000 learning courses, in-house training, a training budget, dedicated coaching, regular check-ins, and professional communities.
• Benefits include a mobility budget or car with fuel or charging, daily and monthly net allowances, eco vouchers, hospitalisation and group insurance, and a bonus scheme.
• Culture emphasizes collective intelligence, reliability, pragmatism, entrepreneurial spirit, ownership, open feedback, and continuous improvement.

Hiring process

• Conversation with the Talent team about background, expectations, and the opportunity.
• Technical architecture conversation covering recent production AI architecture, architecture decisions, system boundaries, non-functional requirements, and the primary technology ecosystem.
• Architecture case or representative challenge followed by a final conversation with technical or business leadership; assessment focuses on architectural proficiency and decision-making.