Senior Data Engineer

The client is a global, private organization operating in the travel & financial services domain, specializing in travel experiences, loyalty programs, insurance, and data driven solutions. The company builds platforms supporting customer engagement, personalization, analytics, and travel safety at a global scale. The organization is currently modernizing its data landscape by developing a modern cloud-based data platform, enabling business and analytics teams worldwide to leverage high-quality, scalable data solutions.

• Experience with Infrastructure as Code (IaC), e.g., Terraform
• Practical experience with NoSQL databases (e.g., MongoDB)
• Knowledge of real-time streaming concepts (Kafka / Kinesis)
• Strong practical experience with Snowflake and dbt
• Familiarity with Agile methodologies (Scrum / Kanban)
• Proficiency in Python, PySpark, SQL, and Bash/Shell scripting
• Hands-on experience with data platforms and tools such as Snowflake, dbt, Kafka/Kinesis, Airflow, AWS Glue
• Experience with cloud platforms, especially AWS
• Experience with Git, GitHub, and GitHub Actions (CI/CD)
• Understanding of modern data architectures: Data Lake, Data Warehouse, Lakehouse, Data Mesh
• Knowledge of data modelling and data governance
• Experience with CI/CD for data pipelines and infrastructure
• Ability to build cost-effective and scalable data pipelines
• English language proficiency minimum B2+

• Design, develop, and maintain scalable data pipelines (ETL/ELT)
• Build and evolve data warehouses and lakehouse/data lake solutions
• Develop and deploy data models supporting business and analytics needs
• Write efficient, scalable code (Python / PySpark)
• Own end-to-end data pipelines feeding the Data Platform
• Ensure high data quality, reliability, and availability
• Work closely with Data Governance (GDPR, CISO, built-in data quality)
• Optimize solutions in terms of performance, cost, and stability
• Collaborate with product, analytics, and business teams globally
• Define and enforce technical and architectural standards
• Prototype and implement modern data engineering approaches
• Align with Data Product Manager on data sources and requirements
• Build and execute a data roadmap for strategic datasets
• Clearly communicate solutions to both technical and non-technical stakeholders
• Mentor junior engineers and support team development
• Represent Data Engineering in cross-functional initiatives
• Promote the value of modern data solutions across the organization