AI Data Engineer III

• You lead, explore and implement the latest AWS and big data technologies to uncover hidden opportunities, enable new capabilities, and build integrations for the SIXT Data Shop
• You partner with Data Engineers, BI Analysts, and Data Scientists to architect optimal solutions for diverse analytical use cases, including dashboarding, ad hoc analytics, data-as-a-product, and machine learning
• You contribute to the Data Platform vision and roadmap through your expertise, innovative ideas, and intellectual curiosity
• You demonstrate exceptional work ethics and integrity when handling sensitive customer data, maintaining the highest standards of data protection
• You design and implement multi-agent workflows that automate end-to-end data operations — from ingestion and transformation to quality validation and distribution — defining clear specs, acceptance criteria, and quality gates so agents execute reliably with minimal human intervention
• You drive the adoption of LLM-augmented data capabilities within the SIXT Data Shop, including RAG pipelines, semantic search over the Data Catalogue, and AI-assisted self-service experiences for internal consumer

YOUR SKILLS MATTER

• You have B.Tech/B.E/ Master’s Degree in Computer Science or similar discipline
• You have atleast 6+ years of professional experience as Senior Data Engineer with experience working on hybrid Data Lake and Data Warehouse architectures, including end-to-end automation of data models from source systems to analytical dashboards using ELT methodologies
• You have expertise with analytical cloud data warehouses (Redshift, Snowflake), data transformation using dbt, and orchestrating interdependent workflows with Apache Airflow
• You have hands-on experience with AI coding assistants and agentic workflow automation. Ability to leverage LLMs with contextual data and RAG (Retrieval-Augmented Generation) systems and demonstrated experience designing agentic architectures where LLM-driven agents are orchestrated across coding, validation, and data quality stages with structured quality gates. Experience with vector and/or graph databases feeding RAG is a strong plus
• You define specs and acceptance criteria that autonomous agents can execute end-to-end, reviews diffs rather than line-by-line output, and encodes quality standards as automated pipeline gates. Experience building or evaluating multi-agent orchestration (coding → QA → validation agents) is a strong plus
• You have ability to decompose complex data problems into structured, measurable specs — including dependencies, non-functional requirements, and edge cases — that agent systems can consume and execute with high reliability. Capable of identifying architectural drift in agent-generated outputs and course-correcting before it compounds
• You have strong foundation in engineering best practices throughout the development lifecycle, including agile methodologies, code reviews, source control (GitHub), CI/CD pipelines (Jenkins), testing, and operations. Deep understanding of data management fundamentals, distributed systems, and data storage/compute principles
• You have advanced proficiency in Python and SQL