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 environment
Build dashboards and reporting tools to enable self-service analytics for the investment team
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:
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 compensation
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
