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

Your Mission

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. This is a two-pillar builder role : 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.

Your Responsibilities

Pillar 1: Data Warehouse & Infrastructure

• Architect, build, and maintain a centralised data warehouse consolidating fragmented data sources across the firm
• Design ETL/ELT pipelines to ingest, transform, and structure data from market databases, deal pipeline sources, and internal systems
• Implement data quality frameworks and governance standards appropriate for a regulated financial services environmentÂ
• Build dashboards and reporting tools to enable self-service analytics for the investment team

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Pillar 2: AI Workflow Development & Internal Tooling

• 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
• Develop internal tools and applications using LLM APIs, integrating with existing systems (CRM, document management, communication platforms)
• Prototype, test, and iterate on AI-powered workflows, translating business requirements into technical solutions under the guidance of senior leadership
• Stay current with the rapidly evolving AI/LLM ecosystem and evaluate and recommend new tools and approaches for implementation

Technical Approach & Stack Expectations

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:

• Data Warehouse: e.g., Snowflake, Fabric
• Cloud: Preferred Azure
• Transformation & Orchestration: dbt / Airflow
• Visualisation: Tableau / Power BI
• Programming: Python, SQL
• AI/LLM: OpenAI, Anthropic APIs; agent frameworks (LangChain, MCP, etc.); workflow automation (n8n, Make)

Your Profile

Required Qualifications:

• Degree in Computer Science, Data Science, Engineering, or a related quantitative field
• 2+ years of professional experience (Specialist: 2–5 years; Manager: 5+ years) in data engineering, software development, or applied data science
• Ideally, at least one end-to-end build 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)
• Strong programming skills in Python; solid experience with SQL and modern data stack tools (e.g., Snowflake, dbt, Airflow)
• Experience with cloud platforms (AWS, GCP, or Azure)
• Familiarity with LLM APIs (OpenAI, Anthropic, or similar) and willingness and experience to build AI-powered workflows (e.g. n8n, make.com)
• Ability to work independently, manage ambiguity, and deliver end-to-end solutions
• Fluent in English; German proficiency is a strong plus

Preferred Qualifications:

• Experience in or exposure to financial services, consulting, or private equity
• Familiarity with agentic AI frameworks (LangChain, CrewAI, or similar) and/or workflow automation platforms (n8n, Make)
• Experience with data visualisation tools (Tableau, Power BI, or similar)
• Understanding of PE workflows (deal sourcing, due diligence, reporting) as context for building effective internal tools
• Track record of building data products or internal tools in a smaller-team environment

Why us?

What We Offer:

• Unique opportunity to build a function from scratch at a leading DACH investment holding
• 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
• Competitive compensationÂ
• Munich-based role with work-from-home options, combined with a collaborative, entrepreneurial team culture
• High autonomy with clear career growth path as the data function scales
• Learning and development budget for conferences, courses, and certifications