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.
