Data Platform Architect

Data Platform Architect

Location: Brussels, hybrid

Level: Senior / Principal

Languages: English required;

Are you the right applicant for this opportunity Find out by reading through the role overview below.
French a plus

About the Team

We're building a data mesh on Azure and Databricks: domain teams own their data as products, and the central platform team gives them a self-serve, governed environment to do it well. We have an MVP platform where pilot teams are already onboarding. The next phase is about evolving it: hardening what works, reworking what doesn't, and building the capabilities the next wave of domains will need.

The Role

You'll take ownership of the platform, its architecture and its evolution roadmap, and lead the team that builds it. That starts with an objective assessment of the current setup — what to keep, what to strengthen, what to rework — and turns into a roadmap, shaped with the team and stakeholders, that balances stabilisation, cost and new capabilities.

Databricks is at the core of the platform, so deep hands-on expertise there — Unity Catalog, workspace topology, Delta, streaming, cost and performance — is essential rather than one skill among many. It's a senior role that mixes architecture, technical leadership and hands-on work — expect a significant amount of your time in the codebase, spiking hard problems and reviewing what the team ships.

We value demonstrated experience to lead the design and implementation over familiarity with the concepts.

What You'll Do

• Assess the current platform objectively — architecture, automation, cost, operations — and turn that into a prioritised roadmap: what to stabilise, what to rework, and what to build next.
• Own and evolve the end-to-end platform architecture — ingestion, storage, transformation, streaming, serving and governance — designing from explicit ASRs and treating cost, resiliency, automation and security as design inputs rather than afterthoughts.
• Own and evolve the Databricks architecture: Unity Catalog design, workspace and catalog topology per domain, compute and cluster policy strategy, Delta and streaming patterns, and the performance and cost standards the team builds to.
• Evolve the platform incrementally: strengthen or rework existing components without disrupting the domains already running on them, and deliver new capabilities in a steady cadence.
• Evolve the data product landing zone: a repeatable, isolated environment per domain covering resource organisation, networking, identity, access boundaries and Databricks workspaces — and guide the team in automating its delivery end to end, so onboarding a new domain becomes routine and new platform features ship without manual builds.
• Define the platform security architecture with the security team: identity and access model, network isolation and private connectivity, secrets, classification and least privilege — designed to pass audit without slowing teams down.
• Drive the data mesh operating model: domain ownership, data product contracts, federated governance, and the interoperability standards and shared definitions that let autonomous domains combine their data reliably.
• Architect FinOps, observability and data quality as platform capabilities:
• FinOps — every domain can see what its data products cost and act on it.
• Observability — mesh health is visible in aggregate, and the impact of a degraded data product is known before consumers report it.
• Data quality — domains own their checks, but the platform provides the mechanism: a sidecar-style capability with scheduled execution, results collected centrally and surfaced in the data governance tool alongside lineage and ownership.
• Ensure the platform integrates with the enterprise data governance tooling [Collibra / Unity Catalog] so catalogue, lineage, quality and ownership are visible in one place.
• Set and hold infrastructure-as-code and automation standards in Terraform through review — you define the patterns;
  the team builds most ofit.
• Produce architecture documentation that makes complex design clear to engineers, reviewers and business stakeholders, and take it through architecture review.
• Lead the platform team: technical direction, backlog prioritisation, thorough design and code reviews, and hands-on support when engineers are blocked.
• Work with data product teams and business stakeholders to understand their needs and shape a sequenced platform roadmap.

What Success Looks Like in the First Year

• Ownership established — you are recognised by the team and stakeholders as the owner of the platform's architecture and direction, and decisions route through you.
• Assessment and roadmap — a clear, evidence-based view of the current platform and an agreed evolution roadmap, shared