Senior Data Engineer

Are you passionate about leveraging cutting-edge AI to revolutionize how individuals interact with their financial data? We're seeking a skilled Data Engineer to join our fintech team and help us build advanced machine learning models to categorize payment transactions and develop a conversational AI system that empowers users with personalized insights into their spending habits.

This role involves working on exciting challenges in a regulated environment, designing robust infrastructure that can operate in our proprietary environment and exclusively within a bank's private systems. If you thrive on creating impactful solutions in financial technology, we want to hear from you!

Who you are:

• Proven track record in data engineering, with a focus on ML model deployment and infrastructure.
• Strong knowledge of Python, SQL, and data manipulation libraries (e.g., Pandas, PySpark).
• Hands-on experience with ML frameworks.
• Proficiency in building and managing secure APIs and microservices.
• Experienced in working with containerized workloads and comfortable deploying and monitoring applications in a Kubernetes environment.
• Knowledge of best practices for data governance, data security, encryption, and GDPR compliance.
• Proven ability to handle complex data challenges and propose efficient, scalable solutions.
• Analytical mindset to understand financial transaction data patterns and user spending behaviours.

Desirable:

• Experience in the fintech or banking industry.
• Familiarity with PSD2 regulations and their technical implications.
• Familiarity with banking statement formats (e.g., MT940, CAMT).
• Understanding of LLM fine-tuning and prompt engineering.

What the job involves:

• Develop and maintain pipelines for fetching and preprocessing financial transaction data from PSD2-compliant interfaces.
• Design data storage and management solutions optimized for ML workflows within highly secure environments.
• Implement ETL /ELT processes to handle diverse banking and other datasets efficiently.
• Build and fine-tune LLMs (Large Language Models) for transaction categorization using financial data.
• Design conversational agents tailored to provide spending insights.
• Collaborate with data scientists to implement and evaluate ML models, ensuring accuracy and explainability.
• Deploy ML prediction services within on-premises and cloud-based banking infrastructures, adhering to strict data residency and security requirements.
• Deploy ML prediction services within our own environments.
• Ensure all infrastructure and systems are designed and implemented with a strong emphasis on security and resilience.
• Monitor and optimize the performance and cost-efficiency of deployed models.
• Work closely with cross-functional teams to deliver robust solutions, including product managers, backend engineers, and compliance experts.
• Provide technical input on product direction and feasibility.

Application process:

• Quick intro call with the hiring manager, understand your background and see if there's potential for a good match. (20–30-minute video call).
• Technical interview: The first part covers a hands-on coding interview where you will have to troubleshoot pre-written code and walk us through the improvement areas and how you would approach the problem (light-live coding, no leetcode or algorithms). The second part is an assessment of your architecture and technical leadership skills in a free-form conversation about what you have done, how you tackled technical and interpersonal problems, and your understanding of basic paradigms in the industry and the trade-offs they bring with them. (90-minute video call).
• Final call with the hiring manager to clarify technical details (contract, working location, etc.) and review the offer together