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Finance Technology
Python and cloud infrastructure by Kacper Michalik applied to finance-shaped engineering problems.
Kacper Michalik’s finance technology focus applies the same production-grade discipline behind his AI/ML, cloud, and back-end work to finance-shaped engineering problems: Python services and data pipelines, AWS infrastructure built for reliability and auditability, and the kind of correctness-first mindset that regulated, high-stakes domains require. It draws directly on shipped work—AWS-certified cloud architecture, back-end services, and applied AI/ML—rather than treating finance as a separate skill set.
Focus
- Python services and data pipelines for finance-shaped workloads (pricing, reporting, reconciliation-style data flows)
- Cloud infrastructure built for reliability, observability, and auditability—not just uptime
- Data pipelines connecting external market or financial data sources to internal systems
- Security- and compliance-aware infrastructure patterns: least-privilege access, environment isolation, auditable logging
Stack
- Python
- AWS (Lambda, ECS, RDS, S3, CloudWatch)
- PostgreSQL
- Docker
- Infrastructure-as-code patterns
- Pandas / NumPy for financial data analysis
Frequently Asked Questions
Has Kacper Michalik shipped a dedicated fintech product?
Not yet as a standalone fintech product. His finance-technology focus is grounded in production experience elsewhere—AWS-certified cloud architecture, back-end services, and applied AI/ML—applied to finance-shaped problems like data pipelines, reliability, and Python-based analysis.
What technologies does he use for finance-related engineering work?
Python and Pandas/NumPy for data work, PostgreSQL for persistence, and AWS infrastructure (Lambda, ECS, RDS, S3) built with the same reliability and auditability standards used across his production systems.
How does this connect to his other engineering work?
Finance technology isn’t a separate track—it’s his cloud architecture, back-end engineering, and AI/ML work applied to a domain where correctness, auditability, and reliable infrastructure matter more than usual.
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