Data and AI Engineering Specialist / Manager (m/w/d)

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{
  "id": "2574856",
  "name": "Data and AI Engineering Specialist / Manager (m/w/d)",
  "office": "Munich",
  "keywords": [],
  "schedule": "full-time",
  "createdAt": "2026-03-24T11:52:00+00:00",
  "seniority": "experienced",
  "department": "Origination",
  "occupation": "other",
  "subcompany": "Armira Beteiligungen GmbH & Co KG",
  "employmentType": "permanent",
  "jobDescriptions": [
    {
      "name": "Your Mission",
      "value": "<span style=\"font-size:14px;\">You will be the technical backbone of Armira’s emerging Data & AI function, responsible for building and maintaining the firm’s internal data infrastructure and AI-powered workflows. <strong>This is a two-pillar builder role</strong>: you will be responsible for (1) designing and implementing Armira’s central data warehouse, ingestion pipelines, data models, and reporting layer, and (2) building AI- and LLM-powered internal tools and workflows that support the investment team’s day-to-day processes, leveraging existing APIs, agent frameworks, and workflow automation tools. Depending on your background, you may lean more toward one pillar initially – what matters is the ability and drive to work across both.</span>"
    },
    {
      "name": "Your Responsibilities",
      "value": "<strong><span style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Pillar 1: Data Warehouse & Infrastructure</span></strong><span style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\"><br></span><ul><li style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Architect, build, and maintain a centralised data warehouse consolidating fragmented data sources across the firm</li><li style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Design ETL/ELT pipelines to ingest, transform, and structure data from market databases, deal pipeline sources, and internal systems</li><li style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Implement data quality frameworks and governance standards appropriate for a regulated financial services environment </li><li style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Build dashboards and reporting tools to enable self-service analytics for the investment team</li></ul><span style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\"> <br><strong>Pillar 2: AI Workflow Development & Internal Tooling</strong><br></span><ul><li style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Design and build AI-powered workflows (e.g., LLM integrations, n8n/Make automation) to automate and enhance deal sourcing, due diligence support, and internal reporting workflows</li><li style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Develop internal tools and applications using LLM APIs, integrating with existing systems (CRM, document management, communication platforms)</li><li style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Prototype, test, and iterate on AI-powered workflows, translating business requirements into technical solutions under the guidance of senior leadership</li><li style=\"font-size:14px;font-family:Arial, Helvetica, sans-serif;\">Stay current with the rapidly evolving AI/LLM ecosystem and evaluate and recommend new tools and approaches for implementation</li></ul><span style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\"><strong>Technical Approach & Stack Expectations</strong><br><br>We expect you to leverage well-established, cloud-native tools and to keep the architecture simple, well-documented, and maintainable, so that another engineer could understand and operate key pipelines within a short onboarding period. We are not optimising for cutting-edge custom architectures, but for pragmatic, robust solutions. The expected tech stack aligns with well-established tools in data engineering:<br></span><ul><li style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\">Data Warehouse: e.g., Snowflake, Fabric</li><li style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\">Cloud: Preferred Azure</li><li style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\">Transformation & Orchestration: dbt / Airflow</li><li style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\">Visualisation: Tableau / Power BI</li><li style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\">Programming: Python, SQL</li><li style=\"font-family:Arial, Helvetica, sans-serif;font-size:14px;\">AI/LLM: OpenAI, Anthropic APIs; agent frameworks (LangChain, MCP, etc.); workflow automation (n8n, Make)</li></ul>"
    },
    {
      "name": "Your Profile",
      "value": "<strong><span style=\"font-size:14px;\">Required Qualifications:</span></strong><span style=\"font-size:14px;\"><br></span><ul><li style=\"font-size:14px;\">Degree in Computer Science, Data Science, Engineering, or a related quantitative field</li><li style=\"font-size:14px;\"><strong>2+ years of professional experience (Specialist: 2–5 years; Manager: 5+ years)</strong> in data engineering, software development, or applied data science</li><li style=\"font-size:14px;\"><strong>Ideally, at least one end-to-end build</strong> of a data product, internal tool, or data platform in a professional setting (e.g., designing a data model, building pipelines, and putting dashboards or an internal application into production)</li><li style=\"font-size:14px;\">Strong programming skills in Python; solid experience with SQL and modern data stack tools (e.g., Snowflake, dbt, Airflow)</li><li style=\"font-size:14px;\">Experience with cloud platforms (AWS, GCP, or Azure)</li><li style=\"font-size:14px;\">Familiarity with LLM APIs (OpenAI, Anthropic, or similar) and willingness and experience to build AI-powered workflows (e.g. n8n, make.com)</li><li style=\"font-size:14px;\">Ability to work independently, manage ambiguity, and deliver end-to-end solutions</li><li style=\"font-size:14px;\">Fluent in English; German proficiency is a strong plus</li></ul><span style=\"font-size:14px;\"><strong>Preferred Qualifications:</strong><br></span><ul><li style=\"font-size:14px;\">Experience in or exposure to financial services, consulting, or private equity</li><li style=\"font-size:14px;\">Familiarity with agentic AI frameworks (LangChain, CrewAI, or similar) and/or workflow automation platforms (n8n, Make)</li><li style=\"font-size:14px;\">Experience with data visualisation tools (Tableau, Power BI, or similar)</li><li style=\"font-size:14px;\">Understanding of PE workflows (deal sourcing, due diligence, reporting) as context for building effective internal tools</li><li style=\"font-size:14px;\">Track record of building data products or internal tools in a smaller-team environment</li></ul>"
    },
    {
      "name": "Why us?",
      "value": "<strong><span style=\"font-size:14px;\">What We Offer:</span></strong><span style=\"font-size:14px;\"><br></span><ul><li style=\"font-size:14px;\">Unique opportunity to build a function from scratch at a leading DACH investment holding</li><li style=\"font-size:14px;\">Deep exposure to the investment team and PE deal-making: you will sit in on deal discussions, portfolio reviews, and strategy meetings, building a genuine understanding of how private equity works and developing your own professional network in the industry</li><li style=\"font-size:14px;\">Competitive compensation </li><li style=\"font-size:14px;\">Munich-based role with work-from-home options, combined with a collaborative, entrepreneurial team culture</li><li style=\"font-size:14px;\">High autonomy with clear career growth path as the data function scales</li><li style=\"font-size:14px;\">Learning and development budget for conferences, courses, and certifications</li></ul>"
    }
  ],
  "occupationCategory": "other",
  "recruitingCategory": null
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