Data Platform Engineer

With annual revenues of $400M, Vireo Health is transforming the healthcare landscape through our ambitious mission to focus on data to drive our business and to build a data infrastructure that helps us scale through acquisitions going from $400M to $1B+ in 2 years. We are building for a highly sophisticated business operation using data for LLMs, ML and analytics. We have a great Saigon AI and data platform team and we are looking to add two data engineers who can help us deliver a sophisticated well-governed data infrastructure from an early stage system and architecture. We are also using LLMs to drive key strategic and operational decisions and are looking for candidates committed to learning and growth with this area. Commitment to Talent: Excellent candidates are able to transfer to our US office after working with us for >1 year (if they want to) ResponsibilitiesSupport our solutions architect and data platform leader (exac in building from scratch a data infrastructure that scales us as we acquire 4-5 companies a year, deploy AI/ML, and scale delivery and retail sales from $400M to $1.5B.Data Warehouse & Lakehouse: Design, implement, and optimize scalable data warehouse and lakehouse solutions (e.g., Delta Lake, Apache Iceberg, Snowflake, BigQuery). Develop ETL/ELT pipelines to efficiently ingest, transform, and manage structured and unstructured data. Ensure data governance, lineage, and security best practices, including access controls and encryption. Optimize data storage, partitioning, and query performance for cost and efficiency.  Data Platform, Cloud & System Reliability: Build and maintain a robust, scalable data platform to support engineering, analytics, and AI workloads. Deploy, manage, and optimize data infrastructure across cloud platforms (AWS, GCP, Azure). Improve system reliability, observability, and cost efficiency through monitoring, logging, and automation. Develop SDKs, APIs, and auto-mation tools to enhance data engineering workflows. Collaborate with Data Analytics, ML Engineers, and Software Engineers to enhance platform capabilities and scalability.  AI Data Workload Implementation: Integrate data with AI workloads, leveraging LLM techniques such as retrieval-augmented generation (RAG) and fine-tuning. Index and manage data within Vector Stores for optimized retrieval and real-time AI applications. Develop external tools and APIs to enable LLMs and AI models to efficiently query and interact with data.