Context of the mission / Objective(s) of the job
The Company Corporate Center is accelerating the transformation of its current data platforms toward a modern Azure Lakehouse architecture.
Today, the environment includes Azure SQL Server on Hypervisor infrastructure with Data Vault modeling.
The target state is a scalable, governed, and high-performing Data Lakehouse platform built primarily on:
- Databricks
- Azure Data Lake Storage (ADLS)
- Delta Tables
- Azure Data Factory (ADF)
We are looking for a Senior Databricks Engineer / Technology Lead to support this journey.
This is not a Data Architect role. We need a hands-on senior engineer with real implementation experience who can design and build solutions, guide best practices, and technically lead engineering teams through
the migration and modernization.
We are looking for someone who is:
- A senior engineer first, not a designer or strategist
- Hands-on and delivery-oriented
- Able to guide teams through implementation challenges
- Comfortable working in complex enterprise environments
- Pragmatic, collaborative, and focused on results
- Strong enough to act as a technology lead without being a formal architect
First months actions
Within the first months, this person will help the company to:
- Synchronize with the Data Architect
- Accelerate Databricks platform adoption
- Establish robust engineering standards
- Deliver reusable ingestion and transformation frameworks
- Improve governance with Unity Catalog
- Enable smooth migration from legacy SQL/Data Vault platforms
- Support reporting use cases through Databricks + Power BI
- Raise the technical maturity of the internal teams
Mission period
Expected Start date: Asap
Duration: 12 months (Depending on project evolution, extension is possible)
Language requirement
| Languages | Speaking | Reading | Writing | Mandatory | Preferable |
| English | 3 | 3 | 3 | X | |
| Dutch | 2 | 2 | 2 | X | |
| French | 2 | 2 | 2 | X |
1 = Basic – 2 = Good – 3 = Fluent
Localisation
Brussels centre.
Required Knowledge and Experience
Personal Skills (soft skills – intangible qualities or traits that enhance our interactions)
Mandatory
- Strong communication and stakeholder collaboration skills
Business experience required (work experience)
Preferable
- Experience with Data Vault migration scenarios.
- Experience with Power BI integration.
- Experience with Infrastructure as Code (Terraform, Bicep, ARM).
- Experience with DevOps pipelines (Azure DevOps / GitHub Actions).
- Exposure to streaming architectures or event-driven pipelines.
Technical experience required (hard skills related to physical or digital tools)
Mandatory
- Proven real-world Databricks implementation experience in enterprise environments.
- Strong hands-on expertise with:
- Databricks Workspace
- Spark / PySpark / SQL
- Delta Lake / Delta Tables
- Unity Catalog
- Databricks Workflows
- Cluster Pool and Serverless
- Strong experience with Azure Data Lake Storage (ADLS).
- Strong experience with Azure Data Factory (ADF).
- Strong understanding of performance optimization and Databricks pricing/cost management.
Functional experience required (job experience in particular industry and in particular function)
Mandatory
- Experience migrating legacy data platforms to modern cloud data platforms.
- Ability to define and implement engineering best practices.
- Ability to technically lead teams while remaining hands-on.
Objective of the job
- Databricks Engineering & Implementation
- Synchronization with the data architect on the Target Architecture and Roadmap
- Lead hands-on implementation of enterprise-grade solutions on the Databricks platform.
- Design and build robust data pipelines using Databricks notebooks, workflows, and Delta architecture.
- Implement scalable ingestion, transformation, and serving frameworks.
- Apply performance tuning and cost optimization techniques across Databricks workloads.
- Ensure reliable CI/CD and deployment practices for Databricks assets.
- Lakehouse Migration & Modernization
- Support the migration from legacy Azure SQL / Data Vault platforms to a Lakehouse architecture.
- Translate existing data structures into efficient Delta-based models.
- Help modernize batch and future-ready streaming workloads where relevant.
- Contribute pragmatic technical decisions during the transformation.
- Storage & Data Engineering
- Implement data solutions using:
- Azure Data Lake Storage (ADLS)
- Delta Lake / Delta Tables
- Medallion architecture principles (Bronze / Silver / Gold)
- Build reusable ingestion and transformation patterns.
- Orchestration & Integration
- Use Azure Data Factory (ADF) for orchestration and integration with enterprise systems.
- Design maintainable scheduling, dependency, and monitoring patterns.
- Governance & Security
- Guide implementation of security best practices on Databricks.
- Support setup and optimization of Unity Catalog structures, permissions, and governance models.
- Ensure alignment with enterprise standards for access control and compliance.
- Technical Leadership
- Act as senior technical reference for internal developers and engineering teams.
- Coach and mentor existing teams on Databricks engineering best practices.
- Promote reusable standards, coding discipline, and knowledge sharing.
- Collaborate effectively with architects, analysts, BI teams, and platform teams.
- Reporting & Analytics Enablement
- Support integration with Power BI using Databricks data sources.
- Help optimize data consumption patterns for reporting and analytics users.
