Senior Software Engineer - Data & AI Platform (m/w/d) in Düsseldorf

Your Responsibilities:

• Build Analytical Query Engines:Connect our transactional backends (MongoDB) with modern analytical event stores (ClickHouse) to support sub-second aggregations across millions of ticket transactions.

• Implement AI Retrieval & Reporting Workflows: Build and deploy Retrieval-Augmented Generation (RAG) pipelines, semantic parsers, and agentic query supervisors that convert natural language prompts into deterministic, accurate SQL and aggregation queries.

• Maintain Low-Latency Reporting APIs: Write high-performance production services in Go or TypeScript/Node.js that power real-time reporting dashboards for global organizers without degrading transactional processing.

• Centralize Data Flows & Auditing: Partner with platform and security teams to build clean data schemas, strict audit trails, and automated evaluation suites that catch hallucinated data or metric drifts before they reach clients.

• Sprint Scoping & Architecture: Collaborate directly in monthly in-person architecture sessions in Frankfurt alongside the VP of Engineering, CTO, and engineering leads to define technical milestones.

• 5+ years of hands-on experiencebuilding high-throughput, distributed production services using Go, Python, or TypeScript/Node.js.

• Analytical Warehousing Experience:Direct, hands-on production experience with ClickHouse (or closely related low-latency analytical engines like DuckDB, Snowflake, or Apache Pinot) for high-volume event streams.

• Database Mastery: Solid practical experience with both document/NoSQL stores (MongoDB) and relational engines (PostgreSQL).

• Production AI / RAG Engineering: Proven experience deploying production RAG systems, function calling, evaluation frameworks, and supervisor architectures (e.g., LangGraph, LlamaIndex, or custom orchestration layers).

• Event-Driven Architectures: Strong understanding of distributed message streaming using Kafka, RabbitMQ, or cloud pub/sub mechanisms.

• Pragmatic Mindset: High autonomy and scrappiness—you choose reliable, deterministic validation layers and battle-tested databases over fragile AI hype.