Who We Are
At the heart of our outsourcing organization, the Data & Intelligence Competence Center serves as a dedicated hub for advanced data-driven solutions. We specialize in data engineering, analytics, and AI-powered insights, helping businesses turn raw information into actionable intelligence. By combining deep technical expertise with industry best practices, we enable smarter decision-making, optimize processes, and foster innovation across diverse sectors.
Building on this foundation, we collaborate with a world-leading reinsurance and risk management company. Our client delivers comprehensive solutions in insurance, underwriting, and data-driven risk assessment. With a strong commitment to innovation and long-term stability, they empower organizations to navigate complex risks and create sustainable value in an ever-changing global landscape. To support this mission, we are seeking a highly skilled AWS Data Engineer to strengthen our data and analytics ecosystem. In this role, you will design, develop, and manage cloud-based data solutions, leveraging big data frameworks to build efficient pipelines, optimize storage, and implement robust processing workflows—ensuring high-quality data availability for analytics and business intelligence.
What You'll Be Doing

• Build and maintain large-scale ETL pipelines using AWS Glue, Lambda, and Step Functions
• Design and manage data lakes on Amazon S3, implementing robust schema management and lifecycle policies
• Work with Apache Iceberg and Parquet formats to support efficient and scalable data storage
• Develop distributed data processing workflows using PySpark
• Implement secure, governed data environments using AWS Lake Formation
• Build and maintain integrations using AWS API Gateway and data exchange APIs
• Automate infrastructure provisioning using Terraform or CDK for Terraform (CDKTF)
• Develop CI/CD pipelines and containerized solutions within modern DevOps practices
• Implement logging, observability, and monitoring solutions to maintain reliable data workflows
• Perform root cause analysis and optimize data processing for improved performance and quality
• Collaborate with business intelligence teams and analysts to support reporting and analytics needs
• Work in cross-functional, Agile teams and actively participate in sprint ceremonies, backlog refinement, and planning
• Provide data-driven insights and recommendations that support business decision-making
  

What You'll Bring Along

• Bachelor’s degree in Computer Science, Information Technology, or a related field (or equivalent experience)
• Minimum 3–5 years of experience in a Data Engineering role
• Strong knowledge of AWS services: Glue, Lambda, S3, Athena, Lake Formation, Step Functions, DynamoDB
• Proficiency in Python and PySpark for data processing, optimization, and automation
• Hands-on experience with Terraform or CDKTF for Infrastructure as Code
• Solid understanding of ETL development, data lakes, schema evolution, and distributed processing
• Experience working with Apache Iceberg and Parquet formats (highly valued)
• Experience with CI/CD pipelines, automation, and containerization
• Familiarity with API Gateway and modern integration patterns
• Strong analytical and problem-solving skills
• Experience working in Agile Scrum environments
• Good understanding of data governance, security, and access control principles
• Experience with visualization/BI tools such as Power BI or AWS QuickSight is a plus
• Excellent command of both spoken and written English.
  

Nice to Have

• Experience designing data products, implementing tag-based access control, or applying federated governance using AWS Lake Formation
• Familiarity with Amazon SageMaker for AI/ML workflows
• Hands-on experience with AWS QuickSight for building analytics dashboards
• Exposure to data mesh architectures
• Experience with container orchestration (e.g., Kubernetes, ECS, EKS)
• Knowledge of modern data architecture patterns (e.g., CDC, event-driven pipelines, near-real-time ingestion)

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