Requirements at a glance

5+ years analytics experience (Required)
Degree in analytics (Required)
BI and data transformation tools (Required)
AI tools (Required)
Data governance (Required)
English (Required)
German (Preferred)

RequiredPreferred

Today

Analytics Engineer (gn)

About LichtBlick:

For more than 25 years, LichtBlick has been driving the energy transition. What started as a pioneer in green electricity has grown into an integrated energy provider with its own generation assets as well as solar, mobility, and heating solutions. Every day, we continue to develop future-oriented energy solutions that combine economic success with social responsibility.

Working model: Remote within Germany, with planned presence at our Hamburg office, particularly for team events and key alignment meetings

LichtBlick is looking for you. Starting now. On a part-time or full-time basis with a permanent contract. For good energy in Technology.

In a decentralized setup, who provides technical support, tools, and training to the domains? Who makes sure transactions are compliant and efficient? Who monitors and reports on the overall health of data operations?

Central Data does. Our work focuses on 4 areas:

Data Platform - enable data nerds by providing a modern, easy to use and robust data stack
Data Governance - ensure secure and efficient data operations
Data Literacy - improve intelligent decision-making
Machine Learning & AI Program - promote AI usage and contribute to LichtBlick’s Strategic AI Program as one of its workstreams.

Your Role:

In our decentralized data setup, the Central Data team enables business-aligned data experts through our self-service platform, governance, and shared standards. As a Senior Analytics Engineer, you develop scalable data models and transformation logic for complex, strategic, and cross-functional requirements. You connect Central Data with the data-owning domains and establish consistent analytics standards across LichtBlick.

Develop and evolve our analytics solutions

Take ownership of complex data models and transformation logic and translate business requirements into consistent, scalable, and reusable data structures
Develop central data models further and ensure their quality, consistency, and long-term usability