Principal Data Engineer

Company Overview: Blink Health is the fastest growing healthcare technology company that builds products to make prescriptions accessible and affordable to everybody. Our two primary products – BlinkRx and Quick Save – remove traditional roadblocks within the current prescription supply chain, resulting in better access to critical medications and improved health outcomes for patients. BlinkRx is the world’s first pharma-to-patient cloud that offers a digital concierge service for patients who are prescribed branded medications. Patients benefit from transparent low prices, free home delivery, and world-class support on this first-of-its-kind centralized platform. With BlinkRx, never again will a patient show up at the pharmacy only to discover that they can’t afford their medication, their doctor needs to fill out a form for them, or the pharmacy doesn’t have the medication in stock. We are a highly collaborative team of builders and operators who invent new ways of working in an industry that historically has resisted innovation. Join us! About the Role: BlinkRx is seeking a highly experienced Principal Data Engineer to design, build, and evolve our core data ecosystem. This role is for a hands-on technical leader who thrives on transforming raw, disparate data into trusted, well-modeled, and analytics-ready assets that power decision-making, AI, and operational systems across the company. As a senior individual contributor within the Data organization, you will own critical aspects of data architecture, modeling, and transformation. You’ll play a central role in unifying data across domains, evolving schemas and pipelines as the business changes, and ensuring our data foundation supports real-time use cases, advanced analytics, and AI-driven initiatives. What You Will Do: Data Architecture Modeling Design and maintain robust data models that clearly define structure, relationships, and business meaning across domains. Establish and evolve canonical data definitions, schemas, and dimensional models to support analytics, reporting, and AI use cases. Ensure data models balance scalability, performance, and ease of use for downstream consumers. Data Transformation Pipelines Build and own end-to-end data transformation workflows, from raw ingestion through curated, modeled, and reporting-ready layers . Implement scalable ELT/ETL patterns that support batch and real-time data processing. Continuously refactor and improve existing pipelines to increase reliability, performance, and clarity as requirements evolve. Data Integration Unification Integrate data from diverse internal and external sources into a unified, consistent data foundation. Resolve data duplication, inconsistency, and fragmentation across systems through thoughtful modeling and transformation. Partner with upstream and downstream teams to ensure clean contracts and reliable data interfaces. AI Advanced Analytics Enablement Design data structures and pipelines that enable AI, machine learning, and advanced analytics use cases. Ensure training, feature, and inference data are accurate, timely, and reproducible. Collaborate with analytics and data science partners to operationalize AI-ready datasets. Change Management Technical Stewardship Lead change management for data systems, including schema evolution, backfills, and migration strategies. Establish best practices for refactoring data pipelines without disrupting critical reporting or consumers. Act as a steward of long-term data quality, maintainability, and technical integrity. Collaboration Influence Partner closely with Analytics, Product, Finance, Operations, and Engineering to translate business needs into scalable data solutions. Communicate complex data concepts clearly to technical and non-technical stakeholders. Set standards and provide technical guidance to other data engineers through design reviews and mentorship. Qualifications: Minimum Requirements 1 ... (truncated, view full listing at source)