QA Engineer
├── Test automation & release validation
├── API, data & integration testing
├── Decision-system validation
├── Python & SQL engineering
└── Quantitative energy-market research
I work at the intersection of software quality, automated validation, decision systems, and quantitative analysis.
My engineering work focuses on turning requirements, data flows, APIs, business rules, and model outputs into testable systems with reproducible evidence. Alongside QA engineering, I maintain independent research and engineering labs covering banking decisioning and Finnish/Nordic electricity markets.
$ ./run_diagnostics.sh --scope engineering
[CHECK] Requirements → testable acceptance criteria
[CHECK] APIs → contracts, errors & integration behaviour
[CHECK] Data → integrity & source-to-output reconciliation
[CHECK] Decisions → eligibility, ranking & explainability
[CHECK] Models → output validation & robustness
[CHECK] Releases → regression evidence & CI quality gates
Principle: verify behaviour, trace evidence, expose failures.
Engineering checklist — illustrative, not a live test report.
I like testing systems where correctness depends on more than a successful HTTP response:
requirements
↓
data integrity
↓
business rules
↓
API & integration behaviour
↓
decision / model output
↓
regression evidence
↓
release confidence
My preferred approach is to validate important behaviour at multiple independent boundaries, reconcile outputs back to source data, include negative scenarios, and make failures visible in CI.
software-quality/
├── test automation
├── API & integration validation
├── data quality
├── decision-system testing
└── release readiness
energy-economics/
├── Finnish & Nordic electricity markets
├── demand behaviour
├── spot-price formation
├── flexibility
├── imbalance
└── balancing markets
