Lead Data Analyst building antifraud, analytics, automation, and AI-enabled operational workflows.
I work on systems that turn noisy data, fragmented meetings, and manual operational processes into structured decisions, reusable tooling, and production-ready execution. My background combines product analytics, risk thinking, internal tools, BigData / ML-driven products, and workflow design for cross-functional teams.
- antifraud and risk analytics in fintech / iGaming
- product and operational analytics
- Python automation for analyst, product, and ops teams
- AI workflows for transcripts, documentation, and requirements
- knowledge systems that connect notes, tasks, and execution
A significant part of my recent work is under NDA. Because of that, this GitHub is intentionally centered on public-safe tooling and workflow patterns that reflect how I actually work in production environments:
- high-volume, messy, real-world data
- strong operational constraints
- coordination across product, analytics, engineering, and risk
- the need to move quickly without losing structure
- turn repetitive operational work into scripts, templates, and internal tools
- make analytics outputs usable outside the analytics team
- convert meetings, incidents, and raw context into reusable working documents
- bridge Markdown-first workflows with Jira / Confluence-style execution systems
- turn ambiguous business requests into concrete, versioned technical requirements
Python crawler for converting websites into an Obsidian-friendly Markdown vault with preserved structure, extracted content, and downloaded images. Useful for documentation capture, research archiving, and knowledge-base migration workflows.
Methodical workflow for turning raw meeting transcripts into structured Obsidian project knowledge. Covers vault structure, Cursor prompt patterns, folder hubs, and pragmatic macOS audio capture tradeoffs.
API-first Flask service for subscriber management, campaign orchestration, tracking, and background email delivery. This repo shows backend workflow design, event modeling, and integration-oriented product thinking.
Python parser for extracting workout metadata and video references from membership platforms. A good example of pragmatic automation around messy real-world content and operational data.
FastAPI service for role-based dialogue synthesis with Qwen3-TTS. This repo represents the AI side of my stack: packaging model capabilities into a usable service layer.
Python tool for creating Jira issues from structured Markdown. Designed for teams that start with analyst notes, incident writeups, or requirement drafts and need a cleaner path into execution.
My work has consistently sat near the boundary between analytics and product execution. Across different environments, I have worked on:
- analytics systems for product and operational decision-making
- BI and reporting products for internal and external users
- BigData and ML-enabled products
- audio / speech analytics and AI-related product directions
- workflow automation around meetings, incidents, task management, and documentation
- internal systems that reduce friction for analysts, product teams, and operations
Core stack
- Python
- SQL
- Pandas
- BI / dashboards
- workflow automation
- AI workflow design
Recurring domains
- antifraud
- risk analytics
- product analytics
- internal tooling
- knowledge operations
- documentation systems
- automate repeatable work first
- keep documentation close to execution
- make analytics useful to non-analysts
- prefer clear operating systems over manual heroics
- optimize for reuse, not one-off outputs
- LinkedIn: rinat-galiamov
- Email:
rinatik66@gmail.com - Telegram: @rinatik66