Senior Data Engineer

📋 Description

• • As a Senior Data Engineer at Leap Technologies, you will be responsible for owning and scaling the data infrastructure that powers critical business decisions across clinical operations, sales, and leadership teams. Your work will directly impact how the company manages specialty drug pricing, patient access, and cost savings for Fortune 500 clients by ensuring data is accurate, timely, and AI-ready.
• • You will design, build, and maintain robust data pipelines and warehouse systems that ingest complex healthcare data from diverse sources — including claims, drug pricing, CRM, and clinical workflows — transforming it into reliable, governed datasets used by both human analysts and AI-driven applications.
• • Leap is a mission-driven, fast-growing innovator in employer specialty pharmacy, partnering with major employers and TPAs to reduce hidden costs in specialty drugs while improving patient outcomes. You’ll join a small, high-ownership engineering team where your contributions have immediate and measurable impact.
• • In this role, you will deepen your expertise in healthcare data systems, AI-ready infrastructure, and scalable cloud data architecture while shaping the foundation for Leap’s next-generation analytics and automation capabilities.
• • Build and own end-to-end data pipelines for claims ingestion, drug pricing synchronization, and CRM integration using Python, SQL, dbt, and orchestration tools like Airflow or Dagster on BigQuery or similar cloud data warehouses.
• • Design and maintain scalable, well-documented warehouse schemas and transformation logic that ensure data quality, consistency, and reliability for downstream reporting, analytics, and AI workloads.
• • Implement comprehensive data quality monitoring including row-count validation, schema drift detection, anomaly alerting, and upstream change detection to ensure trust in data used by clinical, sales, and leadership teams.
• • Develop reporting infrastructure that delivers live, actionable insights to business stakeholders through dashboards and automated alerting systems that surface changes before they become issues.
• • Architect PHI-safe data pipelines that feed LLM agents, automation systems, and AI workloads, ensuring compliance with healthcare data standards while enabling advanced analytics and machine learning use cases.
• • Lead the ingestion and normalization of external data from non-standard, provider-specific formats — adapting quickly to new sources and evolving data structures as Leap expands its network of infusion providers and partners.
• • Collaborate closely with clinical operations, business operations, and engineering leadership to translate domain needs into technical solutions, acting as a trusted data partner across the organization.
• • Mentor and elevate data practices within the team by promoting documentation, testing, and reusable components that increase velocity and reduce technical debt.