Help design, build and continuously improve the clients online platform. Research, suggest and implement new technology solutions following best practices/standards. Take responsibility for the resiliency and availability of different products. Be a productive member of the team. Requirements Hands-on Data Engineer with 4–6 years of experience to design, build, and optimize modern data platforms within complex enterprise and public-sector environments. Work on cloud-native architectures, integrating advanced analytics capabilities, and supporting GenAI/LLM-driven use cases as part of evolving data ecosystems. Design, develop, and maintain scalable ETL/ELT data pipelines Build and optimize conceptual, logical, and physical data models Integrate data from diverse source systems across enterprise landscapes Ensure data quality, governance, security, and GDPR compliance Support cloud data platform implementations and migration initiatives Collaborate with architects, analysts, and BI teams to deliver end-to-end data solutions Optimize platform performance, reliability, and scalability Contribute to Agile Scrum-based delivery teams Support integration of LLMs and generative AI services into modern data architectures Core Data Engineering Strong SQL expertise (complex querying and performance optimization) Hands-on experience with ETL/ELT frameworks Solid knowledge of data modeling principles Experience with cloud data platforms (preferably Azure) Proficiency in Python (or equivalent programming language) Understanding of data governance, privacy, and security principles Amazon S3 (Data Lake storage) AWS Glue (ETL and data cataloging) Amazon Athena (serverless querying) AWS Lambda (serverless orchestration and automation) Amazon Aurora (relational database services) Amazon SageMaker (ML workflows and model deployment) Amazon Bedrock (Generative AI and LLM services) Benefits A challenging, innovating environment. Opportunities for learning where needed.