Databricks Data Engineer

• Design and implement Lakehouse architectures on Databricks (medallion / bronze‑silver‑gold patterns, Delta Lake, Unity Catalog).

• Build robust batch and streaming data pipelines using PySpark and Spark SQL, orchestrated via Databricks Workflows / Jobs.

• Model data for analytics use cases (e.g. star schema for BI, curated datasets for data science).

• Ingest data from APIs, files, databases and SaaS sources into data lakes and lakehouses.

• Implement clear and testable transformation logic (data quality checks, deduplication, SCD, CDC, business rules).

• Apply data governance best practices (cataloguing, lineage, RBAC, masking of sensitive data).

• Work closely with data scientists, analytics engineers and BI developers to make data discoverable, reliable and usable.

• Expose data to downstream tools such as Power BI and other visualization platforms via well‑designed semantic and physical models.

• Support ML and advanced analytics workloads running on top of the Databricks platform.

• Set up CI/CD and deployment pipelines (Azure DevOps / GitHub) for Databricks assets (notebooks, jobs, pipelines, workflows).

• Monitor, optimize and troubleshoot pipelines and clusters for performance, robustness and cost efficiency.

• Help define and implement best practices, coding standards and reusable components within the team.

• Actively share knowledge within the Belgian and Dutch Data & AI community at adesso (brown‑bag sessions, internal talks, blogs, PoCs).

• Contribute to proposals and pre‑sales activities, helping shape new Databricks engagements.

• Coach and mentor colleagues, helping the team grow both technically and professionally.

Databricks, Apache Spark, Azure, Python, PySpark, SQL, Azure Data Lake/Data Factory

You don’t need to tick every box, but you recognize yourself in most of the following:

• Solid experience (min. 5 years) with Databricks and the Apache Spark ecosystem, preferably on Azure Databricks.

• Strong programming skills in Python and PySpark for data engineering.

• Very good SQL skills and understanding of data modeling techniques (e.g. star schema / dimensional modeling; data vault or 3NF is a plus).

• Hands‑on experience building data warehouses / data marts or analytical data stores for reporting and analytics.

• Experience with Azure Data Lake, Azure Data Factory or similar ingestion / orchestration tools is a strong plus.

• Familiarity with CI/CD (Azure DevOps, GitHub Actions) and infrastructure‑as‑code (Terraform or similar).

• Understanding of data governance, security and compliance in the cloud (RBAC, PII handling, auditing).

• Knowledge of analytics / ML concepts and how to prepare data for data science teams is a plus.

• You enjoy talking to stakeholders, understanding their needs and translating them into pragmatic data solutions.

• You can explain complex technical topics in a clear and approachable way, adapted to your audience.

• You are comfortable working in multidisciplinary teams (data engineers, data scientists, cloud engineers, domain experts).

• You are proactive, hands‑on, and take ownership of the solutions you build.

• You communicate fluently in French and/or Dutch and are fluent in English. (both written & spoken)

• This position requires current residency and work authorization in Belgium; relocation from other continents cannot be accommodated.

• A key role in building the Data & AI practice in Belgium, with real impact on our direction and ways of working.

• Close collaboration with our Dutch Data & AI team, giving you access to proven architectures, accelerators and experienced colleagues.

• A personal development plan, training budget and support for Databricks and Azure certifications.

• Opportunities to grow towards roles such as Lead Data Engineer, Architect, Analytics Engineer or MLOps Engineer, depending on your interests.

• A people‑oriented culture focused on collaboration, trust and entrepreneurship.

• Short decision lines and the possibility to influence the technologies, standards and methods we adopt.

• A hybrid way of working with a healthy balance between client‑site, office and home.