Data quality and warehouses
Experience developing data-quality controls and error handling in Azure Data Factory and Databricks environments using Python and SQL.
About MCIFT and Martin Kasala
MCIFT is independent applied research and public research software developed by Martin Kasala. It combines software engineering, data quality, data-warehouse testing, process analysis, Python and cloud-development experience with transparent research into candidate monitoring features.
Relevant experience
Practical grounding for applied research.
Martin has worked as a software engineer, full-stack developer, consultant, test lead and analyst. The experience most relevant to MCIFT is in data quality, automated testing, cloud development and translating operational requirements into measurable controls.
Testing and measurable comparison
Led testing for an international banking data warehouse, automated data controls and reported results systematically to stakeholders.
Full-stack and cloud development
Built web and API solutions with React, FastAPI and Docker, deployed systems to Azure, and applied security, testing and technical-documentation practices.
Process and systems analysis
Modelled AS-IS and TO-BE processes, designed database and workflow models, and translated operational requirements into testable IT solutions.
Project purpose
The purpose is to translate versioned mathematical rules into inspectable candidate features for engineering and digital systems.
Scientific boundary
MCIFT is experimental deterministic research software. Its visualizations and demonstrations are not general validation, certified diagnostics or performance guarantees.
Development approach
Mappings are designed to be transparent, frozen before evaluation and compared with simple and established methods on real data.
Commitment to results
Validation reporting should include improvements, failures, neutral results, false alarms, missed events and uncertainty.
Current models, interpretations and limits.
Public notes explain selected computational models and their limitations. Each note separates the visualization, computational interpretation and validation requirements.