Senior 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.

• 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.

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.

• Degree in Mathematics, Informatics, Natural Sciences or Finance and Economics.
• Minimum 6 years experience in a role of analytics engineer, data analyst or reporting specialist in a fast pace environment and regulated industry.
• Advanced data modelling and data-warehouse development skills
• Knowledgable with data cloud providers and modern data architectures, ideally Snowflake  
• Proficient SQL and Python programming skills
• Good knowledge of data transformation and data orchestration tools, ideally dbt and airflow
• Solid understanding of software engineering principles, including version control (Git), CI/CD, and automated testing
• Very good data visualisation skills, ideally Tableau
• Banking & Domain Expertise:
  • Understanding how double-entry bookkeeping, credit facilities, on/off-balance-sheet items work.
  • Understanding of Integrated Reporting Frameworks
  • Familiarity of banking standard taxonomies. Knowing how to map database attributes to standardized fields required for COREP or FINREP reporting.
  • The ability to translate strict regulatory legal phrasing into programmatic logic.
• Very strong communication skills, to both technical and non technical members.
• Ability to grasp new business concepts and translate them into technical requirements.
• Very good documentation and knowledge sharing skills.
• Adaptability and Continuous Learning.
• Strong negotiation, empathy, and the ability to build consensus across different technical backgrounds.
• Strategic thinking and sense of business criticality and prioritisation.  
• Ability to mentor and inspire team members.
• Excellent English language skills. German language skills is a plus. 
• Comfortable working with AI tools, thinking critically about AI outputs, and contributing to a culture of responsible AI use. We expect you to demonstrate comfort with AI-assisted workflows and a willingness to continuously develop their AI capabilities as the technology evolves.