Data Engineer (AI project)

Jit Team is a Polish company employing over 700 experienced experts, creating highly specialised teams for clients worldwide. They have a long-term partnership with a leading financial group in the Nordic countries, with over 200 personnel engaged in more than 60 projects for this Norwegian financial services provider. The client emphasizes Scandinavian culture, work-life balance, and international teams with daily English language use.

• Experience building and maintaining predictive models supporting lending and risk decisions, such as credit risk, probability of default, and early warning indicators
• Experience with machine learning model performance analysis, data drift, and model degradation
• Proficiency in Python, PySpark, and SQL
• ETL/ELT pipeline development experience
• Cloud data warehouse and data lake experience
• Experience with AWS services including Sagemaker AI and Event Bridge
• Knowledge of data modeling, quality, and lineage
• Experience with ML model development and deployment

Nice to have:

• Corporate lending or financial services background
• Credit decision process knowledge
• LLM/GenAI integration experience
• Agentic AI implementations
• Model explainability

• Design, build, and maintain scalable data pipelines ingesting data from internal banking systems and external data providers
• Develop and optimize data models for corporate lending domains, including loan origination, credit exposure, deal flow, and repayment history
• Implement data quality checks, validation rules, and data lineage to ensure reliable and transparent data
• Manage and evolve the data warehouse and data lake to support analytics, reporting, and machine learning use cases
• Ensure data processing and storage comply with regulatory, security, and data governance requirements
• Work closely with product managers, engineers, and lending domain experts in an agile team
• Translate data insights and model outputs into clear, actionable recommendations for SMEs and business stakeholders
• Document data pipelines, models, and key design decisions to support auditability and knowledge sharing