Energy Data Analyst (Rule Engineer) (f/m/d) - Metrify Smart Metering at Enpal B.V.

Berlin, , Germany -

Full Time

Start Date

Immediate

Expiry Date

23 Apr, 26

Salary

0.0

Posted On

23 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Analysis, Analytics Engineering, SQL, Python, Data Quality, Decision Logic, Data Modeling, Azure, Snowflake, Time Series Data, Agile, Cross-Functional Teams, Documentation, Operational Requirements, Data Workflows, Third-Party Integrations

Industry

Services for Renewable Energy

Description

Company Description We are Metrify Smart Metering, Germany’s largest competitive smart metering operator, and a new strategic spin-off initiative by Enpal. Are you ready for one of the great infrastructure challenges of the 2100s? To truly transform into a decentralized power grid that can support a fully electrified, renewable future, millions of homes need to be equipped with smart meters and control systems. That’s why we are performing the fastest smart meter roll-out in the German market at unprecedented scale, shaping the way this digitally transformed grid will operate. Our mission is to enable utilities, grid operators, and energy providers to install and operate smart meters. Our service portfolio covers all aspects of metering operations: from electricity meter installation and market communication to data management and clearing. While Metrify emerged from Enpal, we now operate independently in the market, serving a broad range of commercial customers, energy suppliers, and municipal utilities. About the Team You’ll be joining a team of 4 engineers, working on projects from scratch. You will be reporting directly to the Technical Product Manager and collaborating closely with non-technical stakeholders and the tech team. Job Description We are looking for an Energy Data Analyst (Rule Engineer) (f/m/d) to join Metrify Smart Metering and take end-to-end ownership to develop mission-critical services, data workflows, and third-party integrations. In this role, you will transform complex operational rules into clear, maintainable decision logic; and build and optimize them in our data platform. You’ll work closely with our Product and Operations teams to ensure our automation is not only correct, but scalable and auditable, forming the backbone of our smart metering platform. You will drive both what needs to be built and how to build it, turning shared ideas into working solutions, with full ownership from idea to deployment. Responsibilities Design and maintain robust data models: You’ll build and validate data models that support regulatory market communication, device operations, and forecasting use cases. Ensure data quality and process reliability: You’ll analyze data quality, detect anomalies, and propose improvements across our metering and backend processes. Translate and operationalize complex rules: You’ll convert regulatory and operational requirements into precise, maintainable, and auditable decision logic within our data platform. Structure and validate business requirements: You’ll transform ambiguous needs into clear, testable rule definitions (e.g., decision tables, condition trees, pseudocode) with strong traceability. Implement and optimize rule-driven logic: You’ll develop, test, and refine logic using SQL and Python within our current tech stack (Snowflake, Azure), ensuring performance and scalability. Collaborate and document: You’ll work closely with product, engineering, and operations teams, while maintaining clear documentation of rule behavior, dependencies, and change history. Qualifications You have at least 4 years of hands-on experience in Data Analysis or Analytics Engineering, ideally in an infrastructure-heavy or regulated environment. You bring hands-on experience with SQL, Python, and large, complex datasets, including raw time series data. You know how to clean and transform data into actionable insights. You also bring experience translating operational rules into decision logic. Familiarity with Azure, Snowflake, dbt, or data modeling concepts (e.g., dimensional modeling, staging layers) is a nice-to-have, as is experience with energy market data flows or smart metering. You communicate clearly in English. Knowledge of German is a strong plus. You enjoy working in agile, cross-functional teams and take ownership beyond writing queries, contributing to the design and improvement of data systems as a whole. Additional Information Benefits: Work in Germany's first green unicorn and actively shape the solar energy revolution. The sun shines all over the world - at Enpal you will find a highly motivated and diverse team with more than 65 different nationalities. Would you rather keep your pet company at home or your colleagues at the office? Even after the pandemic, we offer you a hybrid working model We fulfill every start-up cliché - in our modern office in Berlin-Friedrichshain, you'll find everything your heart desires, from a ping-pong table and yoga corner to a roof terrace and stocked drinks fridges. Your kick-start at Enpal - Get to know the company, your team colleagues and our founder Mario on your onboarding day. Stay up to date - Whether it's company figures at our monthly all-hands meetings or how a photovoltaic system works at the Lunch & Learn, you'll always know exactly what's going on. Energy transition only works together - At Enpal, you can expect a legendary team spirit and unforgettable team events. No mistakes, no progress - We live a strong feedback culture and grow with your input, either personally or anonymously via our feedback tool Culture Amp. At Enpal, we are proud of the diversity of our team. No decisions are made on the basis of skin colour, religion or religious belief, ethnic or national origin, nationality, gender identity, sexual orientation, disability or age, either during recruitment or employment. Enpal stands for a safe workplace and takes action against discrimination and harassment of any kind.

Responsibilities

The Energy Data Analyst will develop mission-critical services, data workflows, and third-party integrations, ensuring automation is correct, scalable, and auditable. Responsibilities include designing data models, ensuring data quality, translating complex rules into decision logic, and collaborating with various teams.