AI Data Engineer III

At SIXT, we are building the semantic foundation that lets AI agents and thousands of analysts query our data platform reliably in natural language. As Senior Analytics/Data Engineer in our Data Platform team, you design the semantic layer and ontologies on top of our AWS-based data platform and build the text-to-SQL agents that turn them into business value. You will join a team of 30 data engineers building the data platform for analytics and AI agents across the SIXT group, working with state-of-the-art LLM technology to shape how our business interacts with data.

Yours to own:

• You define and build our semantic layer (metrics, dimensions, entities) on top of the AWS data warehouse, creating a single source of truth for KPIs consumed by BI tools, analysts, and AI agents.
• You design and implement text-to-SQL agents on the data warehouse, from retrieval and prompt strategy over query generation and validation to evaluation pipelines that measure answer accuracy.
• You specify ontologies and business glossaries for the mobility domain (rental, fleet, pricing, customer) and publish them in machine-readable form so agents and services can consume them.
• You integrate semantic context, metadata, and lineage from our data catalogue into agent workflows, and define the standards that make datasets agent-ready.
• You partner closely with data engineers, analysts, and our AI platform teams, evangelize semantic modeling best practices, and mentor engineers across the platform organization.
• You continuously evaluate emerging tooling (semantic layer engines, knowledge graphs, LLM frameworks) and drive build-vs-buy decisions.

Your skills matter:

• Education You have a B.Tech, B.E., or Master's degree in Computer Science or a closely related discipline.
• Experience You bring 9+ years in analytics/data engineering, including hands-on experience building a semantic or metrics layer (e.g. dbt Semantic Layer, Cube, AtScale, LookML) and productionizing LLM-based applications, ideally text-to-SQL or RAG systems.
• Tech stack You are highly proficient in SQL and Python; you have worked with modern cloud data warehouses (Redshift, Snowflake, or Databricks) on AWS, and with LLM frameworks and APIs (e.g. LangChain, Bedrock, Anthropic API).
• Semantic modeling You have experience in dimensional and semantic data modeling; knowledge of ontology standards (RDF, OWL, SKOS), knowledge graphs, or MCP-based agent context is an advantage.
• Ways of working You work with engineering best practices (Git, CI/CD, code reviews, testing), take ownership end-to-end, and communicate complex concepts clearly to technical and business stakeholders.

Skills

Analytics Engineering, AtScale, Cube, Data Engineering, LLM, LookML, Metrics Layer, RAG, Semantic Layer, Text-to-SQL