Senior Python Engineer - Data & ML Systems
Finclude • Ireland • Greece
No Relocation
Posted: April 9, 2026
Job Description
About us
Finclude is a Dublin-based fintech pioneer revolutionizing how creditworthiness is assessed across Europe. We are bridging the gap for over 200 million people who lack traditional credit access by replacing outdated history-based models with real-time, AI-driven transactional intelligence.
What you’ll be working on
You will work on a Python-based system responsible for:
- Processing and transforming transactional data from multiple banking sources
- Designing data pipelines tailored to real-world data inconsistencies and edge cases
- Building systems for data normalization, categorization, and enrichment
- Supporting the development and integration of scoring models and ML-driven features
- Collaborating with R&D to bring experimental models into production
- Designing scalable, event-driven processing workflows
- Contributing to architecture decisions across data, pipelines, and system design
How we work
- Small team, high ownership
- Direct impact on architecture and product decisions
- Pragmatic approach over over-engineering
- Close collaboration between engineering and R&D
Why join
- Work on non-trivial data problems with real-world impact
- Build systems from the ground up, not just maintain them
- Be part of a team where engineering decisions matter
What we’re looking for
- Strong experience with Python in production systems
- Solid understanding of data pipelines and transformation workflows
- Strong SQL skills and experience with PostgreSQL
- Experience working with messy, real-world data
- Experience integrating or deploying ML/analytics models into production
- Familiarity with event-driven or distributed systems
- Experience with Docker and Kubernetes
- Ability to write clean, maintainable, well-structured code
- Comfortable working in a small, high-ownership team
Nice to have
- Experience with fintech or transaction data
- Experience with cloud platforms (e.g. Amazon Web Services, Google Cloud Platform)
- Familiarity with message queues and event-driven architectures
- Exposure to applied machine learning workflows
Additional Content
About us
Finclude is a Dublin-based fintech pioneer revolutionizing how creditworthiness is assessed across Europe. We are bridging the gap for over 200 million people who lack traditional credit access by replacing outdated history-based models with real-time, AI-driven transactional intelligence.
What you’ll be working on
You will work on a Python-based system responsible for:
- Processing and transforming transactional data from multiple banking sources
- Designing data pipelines tailored to real-world data inconsistencies and edge cases
- Building systems for data normalization, categorization, and enrichment
- Supporting the development and integration of scoring models and ML-driven features
- Collaborating with R&D to bring experimental models into production
- Designing scalable, event-driven processing workflows
- Contributing to architecture decisions across data, pipelines, and system design
How we work
- Small team, high ownership
- Direct impact on architecture and product decisions
- Pragmatic approach over over-engineering
- Close collaboration between engineering and R&D
Why join
- Work on non-trivial data problems with real-world impact
- Build systems from the ground up, not just maintain them
- Be part of a team where engineering decisions matter
What we’re looking for
- Strong experience with Python in production systems
- Solid understanding of data pipelines and transformation workflows
- Strong SQL skills and experience with PostgreSQL
- Experience working with messy, real-world data
- Experience integrating or deploying ML/analytics models into production
- Familiarity with event-driven or distributed systems
- Experience with Docker and Kubernetes
- Ability to write clean, maintainable, well-structured code
- Comfortable working in a small, high-ownership team
Nice to have
- Experience with fintech or transaction data
- Experience with cloud platforms (e.g. Amazon Web Services, Google Cloud Platform)
- Familiarity with message queues and event-driven architectures
- Exposure to applied machine learning workflows