Data Engineer for AI-Driven Fintech Platforms | Hybrid

Vivid Resourcing; Merelbeke; Hybride; EUR 40 000 - 67 000; Plein temps

Avantages offerts par ce poste

Company car or mobility budget

Hybrid work model Kortrijk/Merelbeke

Meal vouchers

Group insurance and hospitalisation

Résumé du poste

Vivid Resourcing is seeking a Data Engineer to design and maintain scalable data infrastructure for a Fintech-focused team. You will enable data-driven decision-making and support AI applications using modern cloud technologies in a collaborative engineering culture.

Responsibilities include building ETL/ELT pipelines, data lakes and warehouses, data integration from multiple sources, and governance with data quality controls.

Qualifications

• 2-5 years of experience as a Data Engineer or similar role.
• Proficient in Python and SQL.
• Experience building production-grade ETL/ELT pipelines.
• Hands-on with Databricks, Snowflake, dbt, Airflow, or similar platforms.

Responsabilités

• Design, build, and maintain scalable ETL/ELT pipelines.
• Develop and optimize cloud-based data lakes, data warehouses, and modern data platforms.
• Integrate data from APIs, databases, SaaS applications, and streaming sources.
• Design reliable and scalable data models to support analytics and business intelligence.
• Implement best practices for data quality, governance, monitoring, and orchestration.
• Build and maintain robust data infrastructure that supports AI and machine learning initiatives.
• Contribute to cloud-native development, CI/CD pipelines, and infrastructure improvements.

Connaissances

Python

SQL

ETL/ELT pipelines

Data modeling

Data governance

Communication

Outils

Databricks

Snowflake

dbt

Airflow

Microsoft Fabric

Azure

AWS

GCP

Docker

Git

CI/CD

Kubernetes

Description du poste

Vivid Resourcing is seeking a Data Engineer to design and maintain scalable data infrastructure for a Fintech-focused team. You will enable data-driven decision-making and support AI applications using modern cloud technologies in a collaborative engineering culture.

Responsibilities include building ETL/ELT pipelines, data lakes and warehouses, data integration from multiple sources, and governance with data quality controls.