Data Engineer (DBX,dbt)

Webellian is a well-established Digital Transformation and IT consulting company committed to creating a positive impact for clients in sectors such as insurance, banking, healthcare, retail, and manufacturing. The project involves building an advanced solution leveraging Large Language Models (LLMs) to scan documents and support automated decision-making processes for a key client in the insurance industry. The role includes collaboration with global stakeholders and business users in a hybrid work model based in Poland.

• Strong experience with Databricks (DBX)
• Advanced knowledge of Python
• Solid experience in building and optimizing ETL/ELT pipelines
• Very good knowledge of SQL and relational databases
• Experience with PySpark
• Knowledge of CI/CD practices and tools (e.g. GitHub)
• General understanding of infrastructure, orchestration, and IT security principles
• Proven experience in Data Engineering (Senior level)
• Bachelor’s or Master’s degree in a technical field (e.g. Computer Science, Engineering) or equivalent experience
• Fluent English (written and spoken)
• DevOps mindset (“you build it, you run it”)
• Ability to understand complex requirements and translate them into actionable solutions
• Strong communication skills and ability to work with cross-functional teams
• Attention to detail, especially regarding data quality and business logic
• Proactive attitude, ownership, and willingness to learn new technologies

Nice to have:

• Experience with libraries such as Pydantic; PydanticAI is a strong advantage
• Familiarity with dbt
• Experience in the insurance domain

• Contribute by building a training dataset for document processing and decision support
• Design and implement end-to-end data pipelines (ingestion, transformation, storage, and consumption)
• Prepare and maintain high-quality training datasets for machine learning models
• Work with large-scale data on a modern cloud data platform (Databricks)
• Apply best practices in data engineering, testing, and deployment
• Collaborate closely with data scientists, engineers, and business stakeholders
• Continuously improve performance, reliability, and automation of data workflow