Data Engineer (AI) banking
⚲ Gdańsk
26 880 - 31 920 PLN netto (B2B)
Wymagania
- Python
- PySpark
- SQL
- ETL
- AWS
- ML model developement
Opis stanowiska
General info:
Rate: 190 PLN net + vat/ hour
Type of contract: B2B, long term cooperation
Model of work: hybrid, 1 day per week work from office (Gdańsk/ Warsaw/ Łódź)
Introduction & Summary
We are seeking a skilled Data Engineer to design, build, and maintain scalable data pipelines, specifically focused on supporting corporate lending operations. The ideal candidate must be proficient in Python, PySpark, and SQL and possess experience with ETL/ELT pipeline development. This role requires a strong analytical mindset, able to transform data insights into actionable recommendations for stakeholders.
Main Responsibilities
• Design, build, and maintain scalable data pipelines for internal banking systems and external data providers.
• Develop and optimize data models for corporate lending domains, including loan origination and repayment history.
• Implement data quality checks, validation rules, and data lineage.
• Manage and evolve the data warehouse and lake for analytics and reporting.
• Ensure compliance with regulatory and data governance requirements.
• Collaborate with product managers and domain experts in an agile setting.
• Document data pipelines, models, and key design decisions.
Key Requirements
• Proficiency in Python, PySpark and SQL
• Experience with ETL/ELT pipeline development
• Cloud data warehouse/lake experience
• Experience with AWS and services (e.g. Sagemaker AI, Event Bridge)
• Data modeling, quality, and lineage expertise
• Experience with ML model development and deployment
Nice to Have
• Corporate lending or financial services background
• Knowledge of credit decision processes
• Experience with LLM/GenAI integration
• Agentic AI implementations
• Understanding of model explainability
Rate: 190 PLN net + vat/ hour
Type of contract: B2B, long term cooperation
Model of work: hybrid, 1 day per week work from office (Gdańsk/ Warsaw/ Łódź)
Introduction & Summary
We are seeking a skilled Data Engineer to design, build, and maintain scalable data pipelines, specifically focused on supporting corporate lending operations. The ideal candidate must be proficient in Python, PySpark, and SQL and possess experience with ETL/ELT pipeline development. This role requires a strong analytical mindset, able to transform data insights into actionable recommendations for stakeholders.
Main Responsibilities
• Design, build, and maintain scalable data pipelines for internal banking systems and external data providers.
• Develop and optimize data models for corporate lending domains, including loan origination and repayment history.
• Implement data quality checks, validation rules, and data lineage.
• Manage and evolve the data warehouse and lake for analytics and reporting.
• Ensure compliance with regulatory and data governance requirements.
• Collaborate with product managers and domain experts in an agile setting.
• Document data pipelines, models, and key design decisions.
Key Requirements
• Proficiency in Python, PySpark and SQL
• Experience with ETL/ELT pipeline development
• Cloud data warehouse/lake experience
• Experience with AWS and services (e.g. Sagemaker AI, Event Bridge)
• Data modeling, quality, and lineage expertise
• Experience with ML model development and deployment
Nice to Have
• Corporate lending or financial services background
• Knowledge of credit decision processes
• Experience with LLM/GenAI integration
• Agentic AI implementations
• Understanding of model explainability
🔍 Dekoder Ogłoszenia
🔴
specifically focused on supporting corporate lending operations
Twoja praca będzie ściśle związana z procesami kredytowymi dla firm, co może oznaczać pracę z bardzo specyficznymi i potencjalnie złożonymi danymi.
🔴
transform data insights into actionable recommendations for stakeholders
Oczekuje się, że nie tylko przetworzysz dane, ale również będziesz aktywnie proponować rozwiązania biznesowe, co wykracza poza typowe zadania inżyniera danych.
🔴
Manage and evolve the data warehouse and lake for analytics and reporting
Może oznaczać odpowiedzialność za całą infrastrukturę danych, w tym jej rozwój i utrzymanie, a nie tylko budowanie pojedynczych pipeline'ów.
🔴
Experience with ML model
Choć nie jest to główny cel roli, może oznaczać konieczność interakcji z modelami ML, ich wdrażania lub przygotowywania danych pod ich potrzeby.
🟡
long term cooperation
Sugeruje stabilność zatrudnienia, ale może też oznaczać brak możliwości szybkiej zmiany projektu lub ścieżki kariery w ramach tej współpracy.