Data Engineer

Overview

In this role you will design and implement data pipelines, apply ML models in production, and collaborate with cross-functional teams to deliver AI-enabled analytics solutions. You will work within a consulting context, combining technical delivery with client engagement to drive data-driven outcomes. The role emphasizes scalable data architectures, monitoring of ML models, and close alignment with business goals. This is a hands-on position with opportunities to shape data and AI capabilities for diverse clients.

Pay / Benefits

• 25 days’ vacation per year
• Private medical insurance
• 3 extra days leave per year for charitable work
• Flexibility and mobility to spend time onsite with clients

• Deploy machine learning models to production and monitor their performance
• Build and manage ETL/ELT pipelines and data flows using batch and streaming tech
• Define and iterate data mappings based on data modelling concepts
• Re-engineer data pipelines for scalability, robustness, automation, and repeatability
• Explore and query large-scale datasets
• Develop data transformation processes, structures, metadata, and workload management
• Identify and resolve data quality, mapping, and database issues
• Connect operational systems with analytics and BI data flows
• Deliver high-quality implementation and documentation for critical functionality
• Deliver code, unit tests, and integration tests
• Participate in agile/scrum ceremonies and collaborate with the team
• Stay updated on latest tech developments, especially generative AI

• Strong proficiency in Python
• Extensive experience with cloud platforms (AWS, GCP, or Azure)
• Experience with data warehousing and lake architectures
• ETL/ELT pipeline development
• SQL and NoSQL databases
• Distributed computing frameworks (Spark, Kinesis)
• Software development best practices including CI/CD, TDD and version control
• Containerisation tools (Docker or Kubernetes)
• Infrastructure as Code tools (Terraform or CloudFormation)
• Strong understanding of data modelling and system architecture
• Experience on at least one AI/ML project
• Knowledge of ML frameworks and models
• Understanding of monitoring ML models in production

• Effective communication, both verbal and written
• Critical thinking and problem solving
• Stakeholder management and collaboration
• Python
• AWS/GCP/Azure
• Data warehousing and lake architectures