About Us
Greenland Commodities is a science and technology-driven trading house based in Prague, operating across European energy markets. We combine deep market expertise with strong engineering and data capabilities to build real-time, production-grade analytics systems that directly support trading decisions.
Our engineering team works closely with traders and analysts to transform complex data into reliable, scalable, and actionable insights.
Position Overview
We are looking for a Senior Data Engineer with strong Data Science and Machine Learning exposure to join our team.
This role sits at the intersection of data engineering and applied data science. You will design and build robust data pipelines while also contributing to ML-driven analytics and decision-support systems.
The focus is not on pure quantitative finance modeling, but rather on building production-ready data and ML systems that operate reliably in real-time environments.
Key Responsibilities
- Data Platform Development
- Design, build, and maintain scalable data pipelines and architectures for large-scale structured and unstructured data.
- ML & Analytics Integration
- Work closely with data scientists to deploy and operationalize machine learning models into production systems.
- Data Processing & Performance
- Optimize data storage and querying performance (high-frequency, large-volume datasets).
- Data Quality & Reliability
- Implement validation, monitoring, and alerting systems to ensure high data quality and system reliability.
- Own the full lifecycle: data ingestion → transformation → model integration → production deployment.
- Collaborate with traders, analysts, and engineers to translate business needs into scalable data solutions.
- Automate repetitive workflows and build standardized pipelines for analytics and reporting.
Qualifications
- Experience
- 3+ years in Data Engineering, Backend Engineering, or similar roles
- Experience working with data-driven systems in production environments
- Programming
- Strong Python skills (data processing + ML integration)
- Solid SQL knowledge (performance optimization is important)
- Data Engineering
- Experience with ETL/ELT pipelines, workflow orchestration, and data modeling
- Familiarity with distributed or large-scale data systems
- Databases
- Experience with systems like Vertica, PostgreSQL, or similar analytical databases
- Machine Learning / Data Science (Important but Practical)
- Understanding of ML workflows (training, validation, deployment)
- Experience working with time-series or predictive models is a plus
- Focus on application and integration, not theoretical research
- Cloud & Infrastructure
- Experience with AWS or similar cloud platforms
- Understanding of cost-efficient and scalable system design
- Mindset
- Strong problem-solving skills
- Product-oriented thinking (building systems that are actually used)
- Ability to work in fast-paced environments
Nice to Have (Not Required)
- Experience in energy markets or commodity trading
- Experience with real-time data systems
- Familiarity with BI tools (Power BI, Tableau)
- Exposure to advanced ML techniques (feature engineering, model evaluation, etc.)
What We Offer
- Competitive salary and benefits package
- A fast-paced, engineering-driven environment
- Direct impact on real trading systems
- Collaboration with traders, data scientists, and engineers
- 4 weeks vacation + 1 extra week
- 3 sick days + 1 birthday leave
- Meal support & public transport contribution
- Modern office in central Prague
Summary (Internal Positioning)
This is not a pure quant role and not a pure data engineer role.
It is ideal for someone who:
- Can build strong data pipelines
- Understands ML systems in practice
- Wants to work on real-time, production trading data
- Prefers impact over theory
