Cloud-Native Data Platform for Real-Time Algorithmic Energy Trading

Overview

I led the design and delivery of a real-time data platform to power algorithmic trading for a leading German energy company.

Launched as a production-ready MVP in under 12 months, this cloud-native platform consolidates real-time data from over 10 mission-critical sources and empowers 10+ full-time traders with a unified, scalable environment. It lays the technical foundation for advanced algorithmic trading strategies and ML-based signal generation.

Problem Solved

Energy markets are increasingly decentralized, volatile, and data-intensive. Traders must react in real time to shifting prices and supply-demand dynamics—yet traditional infrastructure can’t scale fast enough or integrate heterogeneous data sources efficiently.

Solution & Strategic Impact

• Replaced fragmented, manual data workflows with a real-time, cloud-native trading platform.

• Centralized visibility across 10+ volatile energy portfolios, enabling faster and more informed trading decisions.

• Enabled algorithmic trading readiness, paving the way for AI-driven trading strategies.

• Positioned the client as a tech-first leader in the rapidly evolving energy market.

Key Achievements

• Delivered a production-ready MVP from scratch in <12 months.

• Integrated 10+ live data feeds via REST, SOAP, WebSocket, FTP, and AWS SNS—each handling 100k+ datapoints daily.

• Supported daily trading operations of 10+ energy traders with real-time dashboards and alerts.

• Implemented dynamic scaling to meet peak trading demand and ensure zero downtime.

Business Results

• Cut data preparation and monitoring overhead, allowing traders to focus on market strategy.

• Consolidated critical market signals into a unified view—improving decision-making speed and confidence.

• Created a launchpad for ML-based trading algorithms, unlocking long-term competitive advantage.

Technical Highlights

• Cloud-Native Architecture: Built a scalable, event-driven landing zone for high-velocity data ingestion.

• Real-Time Stream Processing: Used Apache Beam (via Cloud Dataflow) to manage 100k+ daily datapoints.

• End-to-End Monitoring: Delivered production-grade monitoring with InfluxDB, Telegraf, and Grafana.

• Fully Automated CI/CD: GitHub Actions-powered pipelines for secure and repeatable deployments.

Tech Stack

Languages & Tools: Python, Docker

Google Cloud Platform:

• Cloud Run

• Compute Engine

• Dataflow (Apache Beam)

• Pub/Sub

• BigQuery

• Cloud Composer (Airflow)

• Cloud Storage

Observability: InfluxDB, Grafana, Telegraf

CI/CD: git, GitHub Actions