Data Analysis, SQL and Fintech Skills

Not isolated buzzwords, but connected skill clusters that feed directly into applied projects and discoverable case studies.

Core skill clusters

Data Analysis and SQL

I turn SQL querying, cleaning, and reporting workflows into decision-support systems. The foreign trade analytics case study shows that capability end to end.

Fintech and Market Research

I connect finance theory with technical indicators, research framing, and product logic. The BIST100 indicator and BIST vs MOEX analysis support this cluster.

Automation and Bot Development

I move repetitive tasks into low-friction interfaces. AskViraBot is the clearest user-facing example.

Multilingual Product and Communication

I design language structure, interface logic, and international reach together. FinTechTerms is the strongest productized example of that skill.

Software and data stack

I connect data, automation, and product layers with Python, SQL, Pine Script, Next.js, Supabase, MS Excel, and Power BI.

AI and API workflows

I use prompt engineering, LLM integration, and API-based tooling inside product development, research, and automation workflows.

Languages and operations

I work in Turkish as a native language, English at B2+, and Russian at B1+; I also connect crisis management, cross-functional team leadership, analytical thinking, and data interpretation with technical delivery.

How the clusters work together

These capabilities are connected rather than isolated. Data analysis builds the research base, market research sharpens the decision frame, automation turns outputs into usable flows, and multilingual product thinking makes the same work discoverable and usable across a wider audience.

Observable project evidence

Each cluster has a corresponding implementation path: data analysis is represented by the foreign trade pipeline, finance research by BIST100 and BIST-MOEX work, automation by AskViraBot, and multilingual information architecture by FinTechTerms. This reduces keyword stuffing risk because the terms are supported by dedicated URLs and real project context.

The page also works as an internal authority layer, letting readers move from capability to project evidence and then to external certificate without leaving the topical cluster.