Data Engineer II (Python & Cloud)

Efficio is the world’s largest procurement consultancy, but we are transforming into a technology-first company. We are moving from "data-based" to "data-driven," building the intelligent backbone that will power our new GenAI and LLM products. As a Data Engineer on the Product Team, you won't just be running reports. You will be building the code that ingests, cleans, and delivers high-quality data to our AI models. This is a role for an engineer who wants to move beyond "scripts" and learn how to build robust, scalable software platforms. Our Engineering Culture: We operate with a DataOps mindset, focusing on automation, observability, and reliability. We don't just "hand off" code to DevOps; we own our pipelines. We invest significantly in your growth, offering sponsorship for AWS Certifications and mentorship from our Principal Architects to help you master Infrastructure-as-Code. This role gives you the opportunity to: Collaborate on the design of scalable cloud infrastructure, utilizing Terraform to provision AWS resources (ECS, Lambda, S3) with a focus on resilience and reproducibility. Build high-performance, maintainable Python applications characterized by strict dependency management, comprehensive error handling, and high test coverage. Own the complete data journey: from ingestion to consumption; designing the API layers and backbones required to integrate GenAI into production. What We Look for: 2–5 years of hands-on experience in Data Engineering or Backend Software Engineering. You are comfortable writing Python functions, handling JSON/API data, and using libraries like Pandas/PyArrow. You understand the difference between a "script" and a "software module". Experience with SQL and an understanding of how databases work (Primary Keys, Joins, Data Types). You prefer writing code over using drag-and-drop ETL tools. You are eager to learn DataOps practices (Docker, Git, CI/CD) Nice to Have Experience with AWS (or equivalent cloud providers) and familiarity with IaC concepts (like Terraform) and a willingness to adopt these patterns is essential. Experience deploying LLMs or working with RAG architectures. Experience with FastAPI or building data-serving APIs. Background in SRE or DevOps.