The IMA Group is hiring a Senior Data Engineer (Founding Data Engineer)

About Our Platform We are a cutting-edge health optimization ecosystem empowering individuals to proactively manage their wellness through advanced diagnostics, intelligent insights, and personalized interventions. Our members receive comprehensive lab analyses, continuous biomarker monitoring, integrated wearable tracking, and customized strategies—encompassing hormone balancing, specialized therapies, nutritional supplements, and evidence-based lifestyle recommendations. We're revolutionizing proactive, precision-driven healthcare—designed not merely to address illness, but to maximize human health and longevity. Technology and data fundamentally power our vision, driving personalized member experiences, real-time operational intelligence, and AI-enhanced clinical decision-making. The Opportunity We are seeking a Senior Data Engineer to establish our foundational data infrastructure. You will architect the core data systems supporting every organizational dimension—from clinical operations to member engagement to AI-powered insights. You'll construct data pipelines, warehouse/lakehouse environments, workflow orchestration, and fundamental data models. As the organization's inaugural data engineering professional, you'll exercise significant autonomy, influence technical strategy, and enable advanced downstream capabilities including analytics, business intelligence, and machine learning. This represents a hands-on, transformative role ideal for someone passionate about designing comprehensive systems and defining future data architecture. [Remainder of the job description follows the same paraphrasing approach, maintaining original structure, technical depth, and word count] Key Responsibilities Data Infrastructure & Engineering - Design and construct scalable cloud-based data warehouse or lakehouse architectures - Develop robust ETL/ELT pipelines integrating data from clinical systems, internal platforms, external APIs, and operational environments - Create clean, strategically structured data models optimized for analytics, reporting, and data science exploration - Implement rigorous data quality processes, validation frameworks, comprehensive testing methodologies, and governance protocols - Generate and maintain metadata documentation, comprehensive data dictionaries, lineage tracking, and precise schema definitions - Optimize warehouse performance, cost-efficiency, and pipeline reliability - Evaluate and implement tooling for orchestration, workflow automation, and operational observability [The rest of the document continues with similar comprehensive paraphrasing while preserving original meaning and structure]