Capabilities

Financial data engineering requires understanding more than databases. Our experience includes the structures and relationships underlying banking data: clients, accounts, portfolios, securities, positions, transactions and their histories.

We also have hands-on experience with Avaloq parametrisation — direct understanding of how financial and operational data originates in one of the core-banking systems widely used in Switzerland, helping us reason about both the financial meaning of banking data and its technical representation.

We design data systems for environments in which reliability, traceability and controlled change matter. Our experience includes Oracle, Snowflake, SQL, Data Vault 2.0, dbt, complex data pipelines, metadata, historization, testing, release management, production monitoring, enterprise automation and operational integration.

We do not build isolated analytical prototypes and hand them over. Our experience includes making analytical systems part of the bank’s production infrastructure, including integration with existing monitoring and automation tooling such as UC4.

Regulatory requirements emerge from an institutional and geopolitical environment that can change rapidly. 282teq combines technical implementation capability with expertise in political economics and institutional change.

The founder completed a doctorate in political economics at the University of St. Gallen with research focused on the Chinese economy, and subsequently applied this institutional perspective to practical banking problems including sanctions. This enables us to approach regulatory questions from both sides: understand the institutional requirement and design the data and analytical processes required to implement it.

The team combines doctoral-level training in economics and finance with strong quantitative expertise: financial econometrics, fixed income, time-series analysis, statistical modelling, volatility modelling, GARCH techniques, Historical Simulation and Filtered Historical Simulation, among other quantitative risk methods.

This provides the foundation for sophisticated analytical and risk-management solutions where conventional reporting and data-engineering approaches are not sufficient.

We use machine-learning methods where they provide a genuine analytical advantage. The team’s experience includes predictive modelling of financial data, doctoral research applying machine-learning methods, and practical work with modern language models and model-adaptation techniques.

AI is another quantitative tool — not a substitute for understanding the financial, institutional or regulatory problem.