Platform Engineer
Solliciteren
Plaats
BrusselsBelgië
België
Contract
Vooltijds, Onbepaalde duurDatum gepubliceerd
2026-03-04Online to:
2026-05-03EUR 20.00 HOUR
Platform Engineer
Job Description: Lead Platform Engineer
Type of contract: Freelance Yearly B2B contract
Location: Brussels-Haren (Hybrid-Once a week min)
Role Summary: We are looking for an experienced and hands-on
Lead Data Platform Engineer
to architect implement, and lead the delivery of a modern,
self-service data platform , guided by the
Data Mesh paradigm . This role is critical to enabling domain teams to build, deploy, and manage high-quality data products independently, enabling decentralized data ownership, and ensuring scalability, governance, and efficiency across the data ecosystem. You will serve as the technical lead, translator of needs, and strategic planner—bridging the gap between data product teams’ pain points and platform capabilities. This role requires deep technical expertise in DevSecOps practices,
Azure Cloud ,
Databricks , and modern data engineering practices. Proven experience in building such platforms is essential.
Key Responsibilities: Architect and implement the data platform infrastructure
end-to-end, including data ingestion, processing, quality, lineage, cataloging, security, and observability layers. Define and execute
a step-wise implementation roadmap
for the self-service data platform—starting from core capabilities to advanced self-service tooling. Engage deeply with data product teams
to understand their challenges and translate those into platform features and reusable solutions. Establish
clear standards, patterns, and templates
to accelerate the delivery of domain data products while enforcing consistency, governance, and quality. Prioritize and manage the platform team’s backlog , balancing business impact, scalability, and feedback from users. Champion and implement the four core
Data Mesh principles : domain-oriented ownership, data as a product, self-serve platform, and federated governance. Implement robust
security, privacy, and access control mechanisms
(e.g., Unity Catalog, Role-Based Access Control) to protect sensitive data. Drive the
development and automation of onboarding processes
for new domains and data products. Lead architectural design reviews, technical roadmap planning, and hands-on engineering work
with the team. Mentor and guide data engineers working on platform and infrastructure components.
Expertise and Skillset Required: Technical Skills: Proven
experience architecting and building scalable, cloud-native data platforms
on Azure Cloud and Databricks. Deep understanding of modern data architecture patterns (e.g., Lakehouse, Data Mesh, Data-as-a-Product). Deep knowledge of Azure components: Data Lake Storage Gen2, Data Factory, Synapse, Event Hubs, CosmosDB, Azure ML etc. Hands-on expertise in Databricks Lakehouse Platform, including Delta Lake, Unity Catalog, DBX, Spark optimisation, notebooks, jobs, and SQL endpoints. Proven expertise in setting-up reproducible platform using Infrastructure as Code (Terraform) Strong programming and scripting skills in Python and SQL; experience with Spark is essential. Experience integrating CI/CD pipelines for data products using tools like Azure DevOps or GitHub Actions. Platform Design & Data Mesh Thinking: Experience implementing the four principles of Data Mesh in real-world environments. Expertise in designing data contracts, access patterns, and versioning mechanisms for data products. Understanding of metadata management, cataloging, and governance frameworks (e.g., Collibra, Unity Catalog, Azure Purview). Platform Leadership: Ability to define phased implementation roadmaps (e.g., MVP > scalable core services > domain onboarding frameworks). Experience leading and mentoring platform engineers, shaping best practices, and driving architectural decisions. Effective at leading a platform engineering team, conducting reviews, and ensuring delivery quality. Proven capability in backlog management, agile delivery, and prioritization based on user feedback and business impact. Stakeholder Engagement: Strong interpersonal and communication skills to collaborate across domains and disciplines. Ability to facilitate workshops and feedback sessions with data product teams and convert insights into technical requirements.
Preferred Qualifications: Background in implementing data platforms in regulated or enterprise environments. Familiarity with dbt, Apache Airflow, or orchestration tools. Experience with observability and monitoring tools for data reliability (e.g., Monte Carlo, Great Expectations, Datadog).
Role Summary: We are looking for an experienced and hands-on
Lead Data Platform Engineer
to architect implement, and lead the delivery of a modern,
self-service data platform , guided by the
Data Mesh paradigm . This role is critical to enabling domain teams to build, deploy, and manage high-quality data products independently, enabling decentralized data ownership, and ensuring scalability, governance, and efficiency across the data ecosystem. You will serve as the technical lead, translator of needs, and strategic planner—bridging the gap between data product teams’ pain points and platform capabilities. This role requires deep technical expertise in DevSecOps practices,
Azure Cloud ,
Databricks , and modern data engineering practices. Proven experience in building such platforms is essential.
Key Responsibilities: Architect and implement the data platform infrastructure
end-to-end, including data ingestion, processing, quality, lineage, cataloging, security, and observability layers. Define and execute
a step-wise implementation roadmap
for the self-service data platform—starting from core capabilities to advanced self-service tooling. Engage deeply with data product teams
to understand their challenges and translate those into platform features and reusable solutions. Establish
clear standards, patterns, and templates
to accelerate the delivery of domain data products while enforcing consistency, governance, and quality. Prioritize and manage the platform team’s backlog , balancing business impact, scalability, and feedback from users. Champion and implement the four core
Data Mesh principles : domain-oriented ownership, data as a product, self-serve platform, and federated governance. Implement robust
security, privacy, and access control mechanisms
(e.g., Unity Catalog, Role-Based Access Control) to protect sensitive data. Drive the
development and automation of onboarding processes
for new domains and data products. Lead architectural design reviews, technical roadmap planning, and hands-on engineering work
with the team. Mentor and guide data engineers working on platform and infrastructure components.
Expertise and Skillset Required: Technical Skills: Proven
experience architecting and building scalable, cloud-native data platforms
on Azure Cloud and Databricks. Deep understanding of modern data architecture patterns (e.g., Lakehouse, Data Mesh, Data-as-a-Product). Deep knowledge of Azure components: Data Lake Storage Gen2, Data Factory, Synapse, Event Hubs, CosmosDB, Azure ML etc. Hands-on expertise in Databricks Lakehouse Platform, including Delta Lake, Unity Catalog, DBX, Spark optimisation, notebooks, jobs, and SQL endpoints. Proven expertise in setting-up reproducible platform using Infrastructure as Code (Terraform) Strong programming and scripting skills in Python and SQL; experience with Spark is essential. Experience integrating CI/CD pipelines for data products using tools like Azure DevOps or GitHub Actions. Platform Design & Data Mesh Thinking: Experience implementing the four principles of Data Mesh in real-world environments. Expertise in designing data contracts, access patterns, and versioning mechanisms for data products. Understanding of metadata management, cataloging, and governance frameworks (e.g., Collibra, Unity Catalog, Azure Purview). Platform Leadership: Ability to define phased implementation roadmaps (e.g., MVP > scalable core services > domain onboarding frameworks). Experience leading and mentoring platform engineers, shaping best practices, and driving architectural decisions. Effective at leading a platform engineering team, conducting reviews, and ensuring delivery quality. Proven capability in backlog management, agile delivery, and prioritization based on user feedback and business impact. Stakeholder Engagement: Strong interpersonal and communication skills to collaborate across domains and disciplines. Ability to facilitate workshops and feedback sessions with data product teams and convert insights into technical requirements.
Preferred Qualifications: Background in implementing data platforms in regulated or enterprise environments. Familiarity with dbt, Apache Airflow, or orchestration tools. Experience with observability and monitoring tools for data reliability (e.g., Monte Carlo, Great Expectations, Datadog).
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