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RABOT Energy DE GmbH
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About us
Rabot Energy is an independent provider of dynamic electricity tariffs for households and businesses. With smart procurement strategies and automated control, we shift flexible consumption into hours with low exchange prices. That way, households and businesses optimise their electricity costs. Our vision is to give all customers the ability to steer their electricity consumption smartly and benefit from low-price exchange hours.
Why Now?
The energy market is going through its biggest shift in 50 years. We no longer just supply electricity, we make it intelligent. RABOT is growing triple digits, and with every new customer, product and partnership, the data we need to make sense of grows too.
We are growing our Analytics Engineering team, and this is a great moment to join early. With AI as a core part of how we work and a clean, modern repo, a motivated person can take on real responsibility fast. This is not a pure dashboard role: you work with dbt and SQL on real models from day one.
Your Mission
You join our Analytics Engineering team and help own the layer between raw data and decisions. You build and maintain models in dbt on BigQuery using dimensional modelling, add tests and documentation, build metrics in our semantic layer and point Metabase at certified models.
Claude Code is a core part of how you work, with skills and agents already set up in our repo. You start by shipping your first models and grow into owning a business domain over time. You learn directly from our senior Analytics Engineer and work closely with the rest of the data team.
Your Results
We think in outcomes, not task lists. If you are successful, here is what that looks like:
Month 3 (ramp-up)
You know the dbt project, the new model structure and the most important source systems, and you work confidently with Claude Code and the skills in the repo.
Your first models are running in production, via branch, review and test, with documentation.
You can trace a core metric (for example revenue or active contracts) from the source all the way to the report.
Month 6 (performance)
You implement model tickets in dbt independently, from requirement to release, with tests and documentation as part of "done".
You build metrics in the semantic layer (MetricFlow) and move Metabase questions onto certified models.
You give and receive reviews, learning from the senior Analytics Engineer and your peers.
Month 12 (scale)
You own a business domain in the model (for example Product or Marketing) and are the point of contact for those teams.
The business teams answer recurring questions on these models themselves, without a ticket.
At mid level, you define metrics together with the business teams and contribute improvements to our standards.
Who We Are Looking For
SQL & modelling: You are confident in SQL (joins, aggregations, window functions) and bring a basic understanding of data modelling (facts, dimensions, granularity).
Curious about the business: You ask about the business logic behind a metric, not just how to build it.
Builder, not just dashboards: You want to work with dbt and SQL on real models, not only build dashboards and reports.
Craft: You know the basics of Git and can explain your own code and the decisions behind it.
AI-native: You are comfortable using AI coding tools (for example Claude Code, Cursor or Copilot) in your daily work.
Experience: First practical experience with data (roughly 0 to 3 years, including working-student roles, internships or a serious own project).
Language: Confident English, which is our working language.
Nice to have: dbt (from a job or your own project), Python, a Git workflow with pull requests and reviews, BigQuery or another cloud data warehouse, a semantic layer (MetricFlow, dbt Semantic Layer or Cube), orchestration (Dagster or Airflow), BI tools (Metabase, Looker or Power BI), experience in the energy market or with subscription and contract business models, experience in a growing startup, and German.
What We Offer
Ownership: You work on real models in production from day one, not on a sandbox.
Modern stack: BigQuery, dbt with dimensional modelling, a semantic layer, Dagster, dlt and Metabase, with Claude Code as a core part of how we build.
Learning: You learn directly from an experienced Analytics Engineer and grow from your first models to owning a business domain.
Culture: Transparent collaboration. We celebrate mistakes as long as we learn from them.
Your Extras
Employee discount on RABOT Energy electricity tariff
Urban Sports Club partnership + Deutschlandticket
Our Contact
Hamburg, Germany
Berlin, Germany
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