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  "job_id": "2INgxFmsoVXKQAjkAAAAAA==",
  "job_city": "Berlin",
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  "job_title": "Data Analyst (m/w/d) - Junior to Midlevel",
  "job_country": "DE",
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  "employer_name": "GreenStone Energy GmbH",
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  "job_posted_at": "vor 18 Tagen",
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  "job_description": "About Us\n\nWe’re a fast-growing energy tech scale-up building a data and analytics platform for utilities and energy providers. Our mission is to help the energy sector make smarter, faster, and more sustainable decisions. By combining data, technology, and sector expertise, we’re enabling utilities and our own teams to operate with clarity and confidence in a rapidly changing industry.\n\nThe Role\n\nAs a Data Business Analyst, you’ll work at the intersection of data, management, and operations. Your job is to ensure decision-makers have the insights they need to run the business effectively and scale efficiently. From analysing performance data to building dashboards that track key KPIs, you’ll turn complex data into clear, actionable insights that inform strategy and operations.\n\nThis is a great opportunity if you’re excited to apply your analytical skills in a high-growth environment and want to see your work directly influence how a scale-up grows and succeeds.\n\nWhat You’ll Do\n• Partner with management and operations teams to understand business priorities and translate them into data-driven analysis\n• Collect, clean, and prepare datasets from internal systems and external sources\n• Write and optimise SQL queries to extract, manipulate, and analyse structured data\n• Build and maintain dashboards, reports, and visualisations that track KPIs, business performance, and operational efficiency\n• Apply statistical and analytical methods to identify trends, risks, and opportunities across the business\n• Support strategic planning, business cases, and performance reviews with robust data insights\n• Ensure accuracy, integrity, and consistency of data used across reporting and decision-making processes\n\nWhat We’re Looking For\n• Experience in data analysis, business analysis, or a similar role\n• Strong foundation in analytics, statistics, and structured problem-solving\n• Proficiency in SQL and experience with relational databases\n• Hands-on experience with visualisation tools (Metabase, Superset, PowerBI or similar)\n• Excellent communication skills, able to explain complex findings clearly to senior stakeholders\n• Strong organisational skills and the ability to manage multiple priorities in a fast-moving environment\n\nWe’re open to candidates at different levels of experience. Whether you’re looking to grow your data career or already confident shaping business strategy with data, we’d love to hear from you.\n\nNice-to-Have\n• Experience with Python, R, or similar tools for data analysis\n• Familiarity with cloud data platforms (AWS, GCP, Azure)\n• Understanding of energy markets, utilities, or SaaS platforms\n• Previous experience supporting business operations or management teams\n\nWhy Join Us?\n• Be a key contributor to how a scale-up grows and operates\n• Work on meaningful challenges at the heart of the energy transition\n• Collaborate closely with senior management, influencing strategy and operations with your insights\n• A fast-paced, supportive, and flexible environment where your contributions have real impact\n• Great opportunities for professional development and career growth\n\nArt der Stelle: Vollzeit, Festanstellung\n\nGehalt: 50.000,00€ - 65.000,00€ pro Jahr\n\nLeistungen:\n• Aktienoptionen\n• Essenszuschuss\n• Flexible Arbeitszeiten\n• Gleitzeit\n• Homeoffice-Möglichkeit\n• Kostenlose Getränke\n• Preisnachlässe auf Produkte/Dienstleistungen des Unternehmens\n• Zusätzliche Urlaubstage\n\nMöglichkeit zu pendeln/umzuziehen:\n• 10963 Berlin-Kreuzberg: bereit sein zu pendeln oder vor Antritt der Stelle einen Umzug zu planen (Erforderlich)\n\nSprache:\n• Englisch (Erforderlich)\n\nArbeitsort: Zum Teil im Homeoffice in 10963 Berlin",
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  "employer_website": "https://gsenergy.io",
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  "job_employment_type": "Vollzeit",
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  "job_posted_at_timestamp": 1772496000,
  "job_posted_at_datetime_utc": "2026-03-03T00:00:00.000Z"
}