Greenland Commodities is a science & technology-based, fast-paced growing trading house, located in the heart of Europe, Prague, CZ. We unite our international trading experiences with science and technology supported by well-educated experts in their niche fields. Our trading desks focus on a variety of trading products; mainly power, gas, oil, coal, and certificates across 20 countries and 12 energy exchanges.
Greenland Commodities empowers its employees through real-time trading experiences, mentors, and value-added strategies in challenging market environments. We are passionately active and trading on each minute of a year.
Greenland Commodities empowers its employees through real-time trading experiences, mentors, and value-added strategies in challenging market environments. We are passionately active and trading on each minute of a year.
QUALIFICATIONS:
- Education: Minimum of a Bachelor’s degree in Computer Science, Engineering or a related field.
- Database Expertise: Proficiency in data tools such as Vertica, PostgreSQL, Redis.
- Data Workflow: Experience with data pipeline, workflow management and ETL systems.
- Cloud Proficiency: Familiarity with AWS cloud services.
- Programming Skills: Proficiency in object-oriented/object-function scripting languages such as Python, Java, C++, Scala.
- BI Tools (a plus): Experience with BI Tools such as PowerBI and Tableau is a significant advantage.
- Problem Solving: Exceptional problem solving skills with a focus on product development.
- Communication: Excellent written and verbal communication skills that facilitate effective collaboration and clear expression of ideas.
Key Responsibilities:
- Development and Maintenance: Create, build, and maintain complex data architectures, including databases and large-scale processing systems.
- Data Quality Assurance: Clean and validate data thoroughly to guarantee accuracy, completeness and uniformity.
- Efficient Data Processing: Develop innovative database systems to efficiently store and process data and optimize performance.
- Collaborative Work: Collaborate effectively with data scientists and architects on a variety of projects, fostering a cohesive and results-oriented environment.
- Streamline Data Processes: Create and implement standardized processes for data mining, modeling and production, increasing operational efficiency.
- Improve Data Reliability: Continuously recommend and implement improvements to increase data reliability, efficiency and quality.
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