EGO Energy
Integrating a distributed energy platform into the enterprise ecosystem
Summary
Supporting EGO Energy's distributed energy division in migrating 1,500+ data points and 70 ETL workloads to a modern cloud infrastructure while raising engineering standards and building a scalable foundation for AI-driven innovation.
EGO's distributed energy group brought pioneering expertise in aggregating electricity from hundreds of distributed power plants, optimizing deployment, and trading on local energy markets, all powered by AI-driven platforms. DEUS joined to support the integration of these innovative AI-powered solutions into EGO's wider enterprise ecosystem, focusing on platform development, data governance, and operational efficiency.
Services
Industry
The Challenge
Bringing a fast-moving energy subsidiary into the enterprise fold without losing the speed and innovation that made it valuable.
Data is fundamental to distributed energy operations. Accurate forecasting, risk assessment, scenario analysis, and regulatory compliance all depend on reliable, well-structured data flowing through the organization in near real time. As the division transitioned into a bigger ecosystem, there was an opportunity to elevate its technical stack, modernizing data pipelines, raising engineering standards, and ensuring the platform could scale to meet enterprise requirements.
Problem 01
AI integration and platform development
- ▪Developing and integrating AI-powered solutions into a scaled broader ecosystem
- ▪Maintaining operational continuity during transition
- ▪Adapting innovative solutions to enterprise standards
- ▪Ensuring platform scalability for growing demands
Problem 02
Data governance and operational efficiency
- ▪Improving data structure, availability, and consistency
- ▪Supporting accurate forecasting and decision-making in a time-sensitive market
- ▪Streamlining processes and establishing robust automation
- ▪Reducing manual effort and improving reliability
The Solution
DEUS engineers embedded alongside the team to raise the bar across three foundational areas, transforming data processing, engineering practices, and operational resilience from the inside out.
01
Data engineering and medallion architecture
Improved data processing methods to enhance performance and auditing, implementing a medallion structure with historical data, validations, and dependency support. Conducted data discovery, defined lineage using a catalog approach, and enabled query federation across data layers.
02
Code refactoring and ML migration
Built reusable Python libraries to abstract data sources and implement migration-proof ML assets. Refactored existing libraries and repositories to improve code standards, modularity, and long-term maintainability across the platform.
03
Infrastructure, observability, and automated testing
With all infrastructure deployed on AWS, implemented robust audit metrics, defined coherent error recovery strategies, and established observability through OpenTelemetry, alongside automated testing frameworks and deployment pipelines.
Impact
70+ ETL workloads migrated to AWS including 50 mission-critical systems, with testing time reduced from 4 hours to 15 minutes.
Migration Scale
70+ ETL workloads migrated to AWS, including 50 mission-critical systems, successfully standardized and deployed with Infrastructure as Code.
Testing Efficiency
Testing time reduced from 4 hours to 15 minutes through migration and automated testing, accelerating development cycles organization-wide.
Scalable Foundation
Standardized processes, enhanced modularity, and expanded engineering capabilities, with pipeline fault tolerance enabling recovery from checkpoints.
Methods
An embedded engineering approach focused on platform modernization, data governance, and operational resilience.
Data engineering
Medallion architecture
Query federation
Data catalog
Python development
ML migration
MLflow
Code refactoring
AWS CDK
OpenTelemetry
Automated testing
CI/CD pipelines
Solution architecture
Engineering
Project management