Praxispromotion: Angewandtes Data Engineering & KI-gestützte Workflows at Optimax Energy
Leipzig, , Germany -
Full Time


Start Date

Immediate

Expiry Date

09 Dec, 25

Salary

0.0

Posted On

10 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Research, Collaboration

Industry

Information Technology/IT

Description

Job Type
PhD Position
Fixed term of three years (with the possibility of extension)
Location
Leipzig, Germany
Workspace
50% at the HTWK Leipzig, 50% at Optimax Energy

ABOUT US

At Optimax Energy, we’ve been developing smart solutions for automated trading systems for over 10 years, driving the shift toward green energy. Today, we are a leading player in the international power trading market.
Our culturally diverse team shares a common vision: we believe extraordinary achievements happen when people are empowered to grow freely. We foster a culture of appreciation, inclusion, and equal opportunity – with a strong emphasis on creative freedom, work-life balance, and long-term collaboration.
In collaboration with Leipzig University of Applied Sciences within the Pro.Motion program, we are offering a hybrid PhD position at the intersection of data engineering, automation, and applied AI – where you’ll explore, design, and evaluate new approaches to integrating complex data systems in energy trading.
You can find the university’s job advertisement here.

YOUR SKILLS

We’re looking for someone who thrives at the intersection of research, engineering, and collaboration – someone who is curious, pragmatic, and excited about building something that has real impact.

Responsibilities
  • Analyze and continuously improve existing data engineering practices and develop innovative data engineering approaches, ensuring robust, scalable, rapid, and maintainable data workflows across the organization.
  • Researching current trends at the intersection of innovative data engineering technology and generative AI.
  • Independently conduct requirements analysis and gather input from stakeholders, aiming for an innovative data engineering environment.
  • Define clear, outcome-oriented goals and validate those goals through experiments and prototypes.
  • Regularly present findings and project updates to internal stakeholders, facilitating feedback loops.
  • Support knowledge transfer within the team through documentation, presentations, and workshops.
  • Actively collaborate across teams to align research outcomes with practical implementation.
  • Collaboration with external partners and users to integrate requirements and validate prototypes in real-world application scenarios.
  • Publication and presentation of scientific results.
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