Senior Analytics Engineer (m/w/d)

Job Description

Rabot Energy is an independent provider of dynamic electricity tariffs with 100% green energy. Through our innovative procurement strategies, we ensure both ecological sustainability and cost optimization in home energy management. By passing on wholesale electricity market prices directly to our customers, we are able to reduce their electricity bills by an average of 35%.

Owners of electric vehicles and smart meters benefit particularly from our dynamic electricity tariff, as they can automatically charge their vehicles at home when electricity prices on the exchange are especially low. In the Rabot Energy app, charging preferences can be easily set and current market electricity prices can be tracked in real time.

With our intelligent charging solutions, we actively contribute to increasing the share of renewable energy in Germany's overall electricity consumption.

Our vision is to give every customer the opportunity to manage their energy consumption in a smart way while conserving the environment and natural resources. By offering smart charging and energy management solutions, Rabot Energy actively helps drive the energy transition forward. With this idea, we have already won the trust of over 100,000 customers.

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 at triple-digit rates - and with every new customer, every new product and every new partnership, the volume of data we need to make sense of grows too.

Our stack is in place: BigQuery, dbt, Metabase. What matters now is the layer in between - clean models, tested logic, unambiguous definitions. A number is only as reliable as the model it comes from. That is where you come in: an experienced person who turns business logic into versioned code instead of into the 200th Metabase question.

Your Mission

You own the layer between raw data and decisions. You model our data in dbt on BigQuery - from staging through core to the marts - keep the models reliable through tests, reviews and documentation, and keep an eye on run times and cost.

Upstream you work with Data Engineering on ingestion, data quality and clear interfaces. Downstream you work with BI and the business functions: you turn business logic into certified, documented models with unambiguous definitions. The point is not that you build every request yourself, it is that analysts and business teams can answer their own questions on your models instead of queuing for analytics capacity.

Your Results

We think in outcomes, not task lists. The points below are what we will look at together in your check-ins after 3, 6 and 12 months - as a shared plan, not as an exam.

Month 3 (ramp-up)

• You know our dbt project and our data landscape - source systems in Sales, Marketing, Product and Customer Support, BigQuery, Metabase - and can trace every key metric from the source to the report.
• Your first models are running in production: via branch, review and test, documented in dbt and our knowledge base.
• You have named the biggest weaknesses in the existing setup - duplicated logic, untested models, expensive queries - and delivered a prioritised proposal for fixing them.

Month 6 (performance)

• You own the model layers for one core domain end-to-end - e.g. Customer & Product: naming conventions, tests, documentation and definitions sit with you.
• Core models are covered by tests (uniqueness, freshness, references); errors surface in CI rather than in a management meeting.
• Business logic lives in dbt rather than in individual Metabase questions and Excel files; duplicated definitions have been removed.
• Cost per model is transparent, and you have measurably reduced the biggest cost drivers in BigQuery.

Month 12 (scale)

• There is one shared, reliable model base for the company's key metrics - versioned, tested, documented, with clear ownership of the definitions.
• Self-service works: analysts and business functions build their analyses on certified models; around 90% of recurring reporting runs through the warehouse and Metabase instead of Excel.
• Releases are routine: CI/CD, automated tests and traceable lineage - changes go live without firefighting.
• Board and investor metrics (churn and retention, cohorts, unit economics) can be reproduced from versioned models at any time.

Who We Are Looking For

• Senior analytics engineer: advanced SQL and data modelling - dbt on a cloud data warehouse (BigQuery), Git-based work, reviews and CI, orchestration (dbt Cloud, Airflow or Dagster) and Python for automation. At least 5 years in analytics engineering, data engineering or BI, owning models yourself.
• Modelling & software craft: dimensional modelling and a semantic layer with well-defined metrics are second nature to you, and you write modular, documented, refactorable models others can read and change. The same SQL copied across ten reports is a problem, not a result.
• Reliability & governance: tests, monitoring, documentation and GDPR-compliant data processing are part of your definition of "done".
• Business & entrepreneurial judgement: you understand our business model and the KPIs behind it (churn, CLV, pricing, spot prices, tariff logic, channels) and model accordingly - and you treat infrastructure as an investment, spotting where compute cost, maintenance or redundancy burn money and prioritising by impact.
• Ownership & role model: you take topics end-to-end, bring order where much is still taking shape, review code, document where people actually look, and help colleagues grow.
• Language: English is our working language. German helps with source systems and market documentation, but it is not a requirement.

Nice to have: experience in the energy market (market communication, meter data, dynamic tariffs), data contracts, infrastructure as code, streaming, experience with investor or board reporting, and scale-up experience in a data-intensive, regulated environment.

What We Offer

• Ownership: you own the layer everything at RABOT rests on - from a squad-level metric to investor reporting - with real room to shape things and a direct line to BI, Data Engineering and the business functions.
• Room to shape: much of this is only just taking shape; you will influence how we model data at RABOT instead of maintaining finished pipelines.
• Culture: we celebrate new ideas and approaches, we take learning seriously, and we understand that life sometimes gets in the way of doing your best work.

Your Extras

• Employee discount on RABOT Energy electricity tariff
• Urban Sports Club partnership + Deutschlandticket

Our Contact

• [email protected]