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Databricks Data Engineering Pipeline
Overview
Built a scalable Databricks data pipeline to automate data ingestion, transformation, validation, and reporting workflows across multiple data sources.
Key Features
Automated ETL/ELT orchestration
Multi-source data ingestion and integration
Medallion architecture implementation (Bronze, Silver, Gold)
Data quality validation and monitoring
Workflow scheduling and dependency management
Optimized processing for analytics and reporting consumption
End-to-end pipeline automation
Business Impact
Reduced manual processing effort, improved data reliability, and enabled near real-time reporting by creating a robust and scalable data engineering framework.
Tools Used
Databricks, PySpark, SQL, Delta Lake, Data Engineering, ETL Automation
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KILLBEROS
Risk-First (Educational · Fully simulated)
Measure the danger before the gain.
Most small investors don't lose for lack of ideas they lose for lack of risk discipline: they over-trade, double a losing position, and over-size out of fear or greed. KILLBEROS gives anyone the risk discipline of an investment fund, on a modest $1,000 budget ($500 in play, €500 kept untouchable). 100% educational, fully simulated no real money, no broker, no orders ever placed.
• Risk Planner — real position sizing on any instrument: size, risk, reward-to-risk, computed live.
• The 7 Gates — every trade must pass 7 risk checks; break one (e.g. a reward:risk below 2:1) and it's BLOCKED. The danger is measured first.
• Market Regime — live market "weather" (real Fear & Greed + real crypto prices) decides which strategies are even allowed to play.
• AI Advisor + a mistake "pokédex" — so the same error never repeats.
• Journal — log real trades, see your true edge: win rate, expectancy, drawdown (real backend, not a mockup).
Innovative Workflow)
KILLBEROS was designed and built in Figma Make, then reviewed live and iterated, version after version (V2 -> V43), by an AI agent "Cowork Brain" working through Figma's MCP.
But the real innovation is HOWdecisions were made: every significant choice (doctrine, a risk gate, a strategy, the UI) was debated by a standing panel of 11 AI "voices" that the agent embodies in one head — 5 trader profiles (macro, chartist, a risk/Aladdin voice with veto power, a short-term "gambler", and a long-term allocator) + 5 engineer profiles (backend, UX, API, security, data).
Risk speaks first, the angles are confronted, a decision is made, and the dissenting view is written down. (Junior) signs off on anything irreversible. It's a small AI team with a real method not a single prompt. Build-in-public: every version and decision is journaled.
And After :
KILLBEROS is the educational tip of the iceberg. Behind it, a far more powerful engine the full GambleRisk is in the works: the same risk-first doctrine, but it paper-trades itself for weeks to PROVE its edge, market regime by market regime, before anything else. Still risk-first, still discipline-before-gain. This is just the beginning.
Team : Junior Odika (+ Cowork Brain, AI collaborator).
URL :
Hey Junior, don't forget to share your project on Instagram, X, or LinkedIn with #ConfigMakeathon and @figma to complete your submission! 😊
Check the guidelines and get that in before the deadline!
Built a Customer Behavior Dashboard analyzing 3.9K customers, tracking average purchase amount, review ratings, revenue by category, and sales by age group. Created using Power BI with interactive filters for gender, category, and shipping type. This project demonstrates end-to-end data analytics from raw data to business insights.
This Excel dashboard analyses £5.6M in retail electronics sales across two store brands: Computech and MicroWorld, spanning multiple UK locations including Aberdeen, Barnsley, and Aldershot. It tracks performance by product category, sales manager, town, and month, giving retail leaders a single view of where revenue is coming from and who is driving it.
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