About Exsolvæ
We are a Brussels-based Data & AI consultancy built around one conviction: the era of the siloed specialist is over. Modern Data & AI projects rarely break because one engineer fails. They break when three excellent specialists ship in isolation and the handoffs collapse. The schema breaks the BI layer downstream. The model lacks the lineage documentation governance will need. The dashboard runs on a pipeline architected for batch.
We hire and grow Solvers. A Solver is a pluridisciplinary Data & AI expert who masters one core domain (Data Foundations, Data Science & AI, or BI & Analytics) and develops a structured understanding of every other discipline in the data flow. When you build a pipeline, you anticipate the BI layer. When you train a model, you account for the AI Act documentation that will follow. That breadth is what we hire for.
This breadth does not happen alone. It happens through Cerebro Sessions, our cross-domain knowledge-sharing forum where every Solver participates and contributes. Cerebro is the engine of collaboration at Exsolvæ: how a Data Foundations expert learns what BI & Analytics needs from a model, how an AI engineer absorbs the governance constraints that will land on a project six months later. Cerebro turns a collection of practitioners into a community, and keeps each Solver broadening their Data & AI knowledge year after year.
What this means for you:
A community of senior, credible peers across data engineering, AI research, computer vision, and time-series modelling. You work alongside people who challenge your thinking and help you grow.
Earned evolution, never negotiated. Titles follow demonstrated maturity. No year-end surprises.
AI-augmented from day one. Your GenAI workspace and the latest tools through our partnerships. We invest in your ability to deliver with AI, not just talk about it.
A real annual Knowledge & Networking Budget for certifications, conferences, and structured networking. We expect you to use it, and we help you plan it.
If this resonates, keep reading.
What we look for?
Technical depth without communication leaves clients confused. Communication without technical depth produces deliverables that miss the architecture. Both, without business affinity, solve the wrong problem.
We hire for all three.
Excellent technical skills. You have shipped production pipelines on the Microsoft and Databricks stack. You can defend your architectural choices in front of a sceptical principal engineer. Your code is review-ready, not "works on my machine". You read papers and release notes when the project warrants it.
Very good soft skills. You write a Slack message that does not need a follow-up call. You can disagree without escalating. You handle stakeholder push-back without losing the architectural intent. You leave a meeting with a written summary of who owns what.
Business affinity. You understand that a data pipeline exists to serve a business decision, not the other way around. You ask why a metric matters before optimising it. You translate technical trade-offs into business consequences a non-technical stakeholder can act on. You see the engagement through your client's P&L as well as through your code.
The Role:
You will operate inside modern data platforms of our clients across Belgium. The mandate is to design, build, and run the data foundation that powers their AI, agentic systems, and decision intelligence programmes.
This role is for builders who ship. You will deliver pipelines that ingest, transform, and govern data under regulatory pressure and at scale.
What will you do?
* Design and build production-grade pipelines on Microsoft Fabric (OneLake, Direct Lake, Mirroring) and Azure Databricks (Lakeflow, Lakebase, Unity Catalog), the two platforms that anchor our Belgian enterprise engagements.
* Architect medallion-style lakehouses (Bronze, Silver, Gold), applying Data Vault 2.0 for auditability or Kimball where analytical performance is the priority.
* Implement transformation logic in dbt and PySpark, version-controlled, tested, and documented to a standard that survives external audit.
* Build CI/CD pipelines (Azure DevOps, GitHub Actions, or GitLab CI) with Terraform for infrastructure-as-code.
* Operate in regulated environments (GDPR, NIS2, EU AI Act) and embed governance through Unity Catalog, Microsoft Purview, or Collibra.
* Build the data layer for agentic and GenAI workloads: vector store ingestion, embedding pipelines, RAG-ready datasets, semantic contracts for AI agents.
* Translate stakeholder questions into data products used in production, not dashboards that get archived.
* Mentor junior engineers through how you write code reviews.
The Technical Stack
Senior generalists who can pick up new tools quickly are preferred over narrow specialists. Our anchor is the Microsoft and Databricks ecosystem. If you have shipped production workloads on Microsoft Fabric or Azure Databricks, we want to talk.
Cloud and data platform.
Microsoft Azure as the foundation: ADLS Gen2, Azure Data Factory, Synapse Analytics. Microsoft Fabric (OneLake, Direct Lake, Mirroring, Real-Time Intelligence, Fabric IQ). Azure Databricks (Lakeflow, Lakebase, Unity Catalog, Genie, Mosaic AI).
Languages.
Python (advanced, including async, typing, packaging). SQL and T-SQL (advanced, including window functions, CTEs, performance tuning). PySpark (advanced). Scala is a plus.
Transformation and orchestration.
dbt (mandatory: the 2026 standard for governed SQL transformations). Azure Data Factory, Databricks Lakeflow, Airflow, Prefect.
Data modelling.
Data Vault 2.0, Kimball dimensional modelling, Medallion architecture, semantic layer design. You can defend the choice between the three in front of a sceptical architect.
Streaming and real-time.
Kafka, Azure Event Hubs, Change Data Capture, Real-Time Intelligence in Fabric.
AI and agentic data layer.
Vector stores (Azure AI Search, pgvector, Mosaic AI Vector Search, Weaviate). Embedding pipelines and RAG architectures. Azure OpenAI and Azure AI Foundry. Exposure to LangChain, LangGraph, Model Context Protocol, Databricks Agent Bricks, or Azure AI Agent Service is highly valued.
Governance and quality.
Unity Catalog, Microsoft Purview, Collibra, Coalesce. Great Expectations, Soda, dbt tests. Monte Carlo or equivalent observability is appreciated.
DevOps and platform.
Terraform. Docker. Kubernetes is a plus. Azure DevOps, GitHub Actions, YAML release pipelines.
Reporting and analytics.
Power BI as primary (Direct Lake, semantic models, DAX, Power Query). Tableau exposure is welcome.
The Mindset we are looking for:
We hire for how you operate, not just the tools you know. Six traits describe how Solvers work, individually and together. They guide how we hire, mentor, and grow.
You ship.
You build pipelines that run in production, not POCs that decorate a slide. When a regulator asks for lineage, you show it. When a stakeholder asks where a number came from, you can trace it.
You read the room.
You can sit with a non-technical CDO and a principal engineer who reads model architectures, and adjust your register accordingly. Same content, different translation.
You are scientifically rigorous.
You do not overclaim. If you have not benchmarked it, it is not state-of-the-art. If you have not deployed it, it is not production-ready. You write down what you do not know.
You take ownership end to end.
From requirements with the business sponsor on Monday to production monitoring at 2am on Friday if needed. The pipeline is yours until it is documented and handed off.
You are comfortable in regulated environments.
GDPR, NIS2, and the EU AI Act are not surprises. They are the operating context. You design for auditability from the first line of code.
You mentor.
Junior engineers learn from how you write code reviews, not from a separate mentoring meeting.
Languages
Fluency in French, English, and Dutch is required, spoken and written. You will run meetings in Dutch, write technical specs in English, and brief francophone stakeholders in French in the same week. This is a firm requirement of the role.
What makes you stand out?
* A Master's or PhD in Computer Science, Information Management, Applied Mathematics, Physics, or Engineering.
* Microsoft Azure Data Engineer Associate, Microsoft Fabric Analytics Engineer Associate, or Databricks Certified Data Engineer Professional certification.
* Production exposure to agentic AI deployments (Databricks Agent Bricks, Azure AI Foundry, Azure AI Agent Service, or equivalent).
* Public artifacts: papers, patents, open-source contributions, conference talks (FOSDEM, Data Innovation Summit, Devoxx).
* Track record of delivering complex data platforms at enterprise scale, ideally in regulated environments.
* Comfort with at least one regulated vertical: financial services, life sciences and pharma, public sector, critical infrastructure.
What Exsolvæ Offer:
No layers, no internal politics, no corporate dilution.
Transparent compensation. Salary discussed openly at first interview, with progression aligned to the seniority track. Bonus Contributions are structured through our Solver Incentive Program.
Senior individual contributor freedom. You spend your day on the engagement, not on internal reporting or account management overhead.
Engagement selection. We say no to mandates we cannot deliver well. Predictable portfolios, hybrid or on-site depending on the client.
Multiple growth paths beyond the seniority ladder. Responsibility mandates (Lead Solver, Core Domain Leader, Competence Node) recognise different forms of contribution.
Time set aside to publish, contribute to open source, or speak at conferences when the work warrants it. The Knowledge & Networking Budget covers it.
Feel like a Solver?
Reach out.