Bank Analytics Engineer

Solaris is Europe’s leading embedded finance platform, pioneering Banking-as-a-Service to bridge the gap between financial technology and licensed banking. Founded in 2016 and headquartered in Berlin, Solaris enables fintechs, digital ecosystems, and multinationals to embed accounts, cards, lending, and payments directly into their own products, creating seamless financial experiences for their users. As a fully licensed German bank, Solaris combines the highest standards of IT security and regulatory resilience with a modular, highly scalable infrastructure.

Today, Solaris is driving the next evolution of financial services by transitioning into an AI-native banking platform, ensuring European businesses can deploy secure, adaptable financial products at scale.

Why join Solaris?

• 🚀 We are fundamentally redesigning banking processes around AI orchestration, standardized modular building blocks, and embedded regulatory compliance.
• 🎯 We combine tech and banking in dedicated hubs - driving the technology infrastructure out of Berlin and banking operations out of Frankfurt.
• 💡 Through our internal mobility, growth opportunities, and the Solaris Academy, we offer continuous learning tracks, AI ambassador mentorship, and upskilling to keep your skills ahead of the curve.
• ⚖️ We foster a workplace rooted in integrity, proactive risk management, and equality actively driving initiatives like DEI initiatives.

Your Role

• Design and implement robust, clean and reusable data models for the Risk, Regulatory Reporting and Finance functions that centralise reporting of financial instruments (credit cards, loans, bonds, deposits etc.) following the BIRD and IReF standards.
• Define process steps and tasks  to fulfil CoRep(Credit Risk, Market Risk, Operational Risk), FinRep (BS, income statements using IFRS and HGB), Liquidity Reporting (LCR, NSFR) in close collaboration with Risk, Regulatory Reporting and Finance functions and guide the departments towards better data coverage for informed business decisions and critical reporting.
• Manage standardised metrics in the form of a semantic layer to serve as single points across reporting, data exploration and business intelligence.
• Reconciliation Automation: Build scheduled matching programs to cross-verify the bank’s internal ledgers
• Implement and maintain efficient incremental loading strategies to manage high-volume, time-series financial data. 
• Contribute towards the common data quality framework by applying patterns and tests.
• Constantly document the processes and technologies and enable knowledge sharing.

We'd love to see 

Depending on your level of experience, your responsibilities and scope of role will range. We don’t care much about fancy titles, but rather about real personal and professional development, as laid out in our learning framework. Let’s figure together out how you can contribute to our team.