Data Engineer

In Solita’s AI, Analytics & Agents community, we support our customers throughout their data journey, from building robust data foundations to enabling data-driven decision-making across the organization. We believe that impactful data solutions are created through multidisciplinary collaboration, combining solid data engineering practices, modern cloud technologies, and scalable architectures. Our data platforms increasingly run on Databricks and modern Lakehouse patterns. You don’t need to be a Databricks expert yet, if you bring strong cloud data engineering skills, we’ll help you grow into a Databricks specialist through a clear learning path and hands-on project work. We take a holistic approach to our customers’ challenges and help them build sustainable, production-ready data solutions that enable analytics, insights, and – where relevant – AI capabilities. Your role in a nutshell As a Data Engineer, you will design and build scalable data platforms and pipelines that power analytics and data products – and increasingly, Databricks-based Lakehouse solutions. Your work will include developing robust data architectures, implementing data pipelines, and building the foundations that other teams (analytics, data science, AI) can rely on. You will be involved in all phases of projects: from understanding data requirements and architecture design to production deployment and continuous improvement. In practice, your work will include: – Designing and implementing scalable, production-grade data pipelines (batch and/or streaming) on cloud platforms (Azure, AWS, GCP)– Building and maintaining modern cloud data platforms using technologies such as Databricks (or, depending on the customer, Snowflake, Azure Synapse, or AWS data services)– Implementing data modeling approaches (e.g., Data Vault 2.0, dimensional modeling, or schema-on-read patterns)– Ensuring data quality, governance, security, and performance optimization across the data platform– Consulting and providing technical guidance to customers on data architecture and platform strategy– Working as part of a team of experienced data engineers, architects, and data professionals Solutions are built collaboratively, but in this role you will have the opportunity to take ownership and influence data architecture and solution design from the very beginning. Your Databricks growth path at Solita You are not required to know Databricks on day one. What matters most is solid data engineering experience and willingness to learn. At Solita, you will have: – A ‘structured learning path’ for Databricks (self-study material, internal trainings, certifications)– Hands-on learning in projects together with experienced Databricks engineers– Support from our ‘Databricks community’ and peers across countries– Possibility to deepen into topics like Lakehouse architectures, Delta Lake, and Spark optimization over time If you already have Databricks experience, that’s a strong plus – but it’s absolutely not a blocker if you don’t. What helps you succeed in this role Core data engineering experience – Several years of hands-on experience designing and building production-grade data pipelines and cloud data platforms Cloud expertise – Strong experience with at least one major cloud platform (Azure / AWS / GCP)– Experience building data infrastructure using IaC tools (e.g., Terraform, Bicep, CloudFormation) Data technologies – Hands-on experience with modern data platforms and tools such as Databricks, Snowflake, Spark, Delta Lake, or cloud-native data services– If you don’t yet have Databricks experience, you bring strong skills in similar technologies (e.g., Spark, data warehouses, cloud data services) and are motivated to learn Databricks with our support Programming skills – Proficiency in Python and SQL– Experience with Scala, Java, or C# is beneficial Data modeling & architecture – Experience with data modeling techniques (e.g., dimensional modeling, Data Vault, wide tables, or similar)– Understanding of data architecture principles and best practices DevOps & ways of working – Familiarity with CI/CD processes, Infrastructure-as-Code, and DevOps ways of working (e.g., Docker, Kubernetes, Git, automated testing) Consultative approach – Ability to listen, discuss, and translate business requirements into robust data solutions– Comfortable working in a customer-facing, collaborative environment Languages – Fluent in French or Dutch, and English Experience with AI/ML workloads (feature pipelines, model-serving data, MLOps) is a plus, but not required. Curiosity about how data platforms enable AI is more important than being an AI specialist yourself.