<div class="show-more-less-html__markup show-more-less-html__markup--clamp-after-5 relative overflow-hidden"> <p><strong>Role Overview</strong></p><p><br/></p><ul><li><strong>Location:</strong> Berlin, Germany (Hybrid)</li><li><strong>Language Requirement:</strong> Professional German (B2+ minimum) and fluent English</li><li><strong>Target Sector:</strong> Real Estate, PropTech, or adjacent data-heavy corporate sectors (such as Macroeconomics, Market Analysis, or Investment Management)</li></ul><p><br/></p><p><strong>Position Summary</strong></p><p>Our client is a premium, multi-national commercial firm seeking a senior, hybrid Data professional who thrives at the intersection of <strong>Data Engineering, Data Science, and Modern AI.</strong></p><p><br/></p><p><strong>Implementation</strong></p><p>As the company finalizes its migration to a modern Snowflake cloud architecture and harmonizes its regional data systems, the successful candidate's primary mission will be to connect structured data pipelines with intelligent natural language retrieval systems. The engineer will be instrumental in building the infrastructure that allows internal AI assistant interfaces to securely, accurately, and autonomously query databases, translating raw corporate data into trustworthy insights and visual outputs for cross-functional teams.</p><p><br/></p><p><strong>Key Responsibilities</strong></p><p><br/></p><p><strong>1. AI Integration &amp; Database Connectivity</strong></p><ul><li>Design and implement systems that securely connect LLMs to large-scale cloud data warehouses (e.g., building Text-to-SQL workflows or Retrieval-Augmented Generation / RAG frameworks).</li><li>Enable advanced data outputs from natural language queries, such as integrating geospatial data to display interactive maps based on specific localized financial thresholds.</li><li>Collaborate with broader digital enablement teams to ensure regional data solutions scale effortlessly into a wider, pan-European database framework.</li></ul><p><br/></p><p><strong>2. Data Engineering &amp; Analytics Pipeline</strong></p><ul><li>Work alongside the core data engineering team to clean, structure, and format incoming datasets, optimizing them specifically for AI-driven retrieval and analytical modelling.</li><li>Maintain rigorous data quality, consistency, and transparency across high-priority corporate reporting metrics, including ESG (Environmental, Social, and Governance) data.</li><li>Formulate advanced analytical solutions to support research teams with market intelligence reports and data storytelling.</li></ul><p><br/></p><p><strong>3. Resolving the AI Quality &amp; Validation Gap</strong></p><ul><li>Develop robust validation methods and benchmarking systems to ensure AI-generated data insights are completely accurate and auditable.</li><li>Implement strict governance guardrails within the LLM retrieval process to prevent hallucinations and maintain strict compliance with corporate data security standards.</li></ul><p><br/></p><p><strong>Required Experience &amp; Skills</strong></p><ul><li><strong>Professional Background:</strong> Roughly 5 to 10 years of experience operating within data analytics, data engineering, or machine learning engineering environments.</li></ul><p><br/></p><p><strong>Technical Stack:</strong></p><ul><li>Strong proficiency in <strong>Snowflake</strong> and cloud data warehousing infrastructure.</li><li>Expertise in <strong>Python</strong> and advanced SQL.</li><li>Hands-on experience with AI/LLM orchestration frameworks (such as LangChain or LlamaIndex) and API integration.</li><li>Familiarity with data visualization platforms and GIS mapping tools.</li><li><strong>Industry Knowledge:</strong> A solid understanding of market dynamics, asset management, portfolio strategies, or macroeconomic analysis is highly preferred.</li><li><strong>Professional Mindset:</strong> An independent problem-solver capable of evaluating a database, understanding the underlying business logic, and autonomously structuring data architectures to extract strategic corporate value.</li></ul> </div>