Senior Data Platform Engineer II

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 We are seeking a Senior Data Platform Engineer II (Staff-level equivalent) to define and evolve an AI-first real-time and batch data platform built on AWS and Databricks. This role owns the technical vision for how data is ingested, processed, governed, and served as trusted, AI-ready datasets, features, metrics, and APIs that power intelligent products, decisioning systems, machine learning models, and operational workflows. As a Senior Engineer, you are a technical authority and force multiplier —deeply hands-on while setting architectural direction across streaming systems, lakehouse design, and data-serving layers optimized for AI, ML, and real-time intelligence . You will partner closely with engineering, analytics, data science, and product teams to simplify the platform, eliminate legacy patterns, and establish scalable, reliable foundations for AI-driven use cases and real-time analytics . What You Will Do Own the end-to-end AI-first data platform architecture , spanning streaming ingestion, lakehouse storage, feature generation, and data/insight serving layers Architect real-time streaming systems using AWS Kinesis and Spark Structured Streaming to support low-latency AI, ML inference, and decisioning use cases Design stream-to-lakehouse convergence patterns that unify real-time and historical data with strong correctness guarantees, enabling both analytics and ML workloads from a single source of truth Build and evolve data, metrics, and feature APIs that expose curated, AI-ready datasets for downstream applications, analytics, and machine learning models Establish canonical event schemas, feature definitions, and data contracts to support event-driven systems, model training, and online/offline feature consistency Make deep technical decisions across AWS infrastructure (Kinesis, S3, IAM, networking) and Databricks internals (clusters, jobs, Delta Lake, performance tuning), with an emphasis on AI scalability and cost efficiency Drive platform modernization , retiring legacy tools and patterns in favor of lakehouse-first, AI-native designs Set standards for high-performance SQL and Spark workloads , optimizing for cost, latency, scale, and downstream AI consumption Lead complex platform initiatives from architecture through production delivery , ensuring reliability for both real-time analytics and AI systems Provide technical leadership and mentorship, shaping best practices for AI-ready data modeling, data quality, observability, and platform operability Technical Skills Deep expertise in real-time and distributed data systems , including AWS Kinesis and Spark Structured Streaming Strong command of Databricks on AWS (Delta Lake, clusters, jobs) and core AWS services (S3, IAM, networking) Proven experience designing data- and feature-serving architectures and APIs for analytics, metrics, and machine learning c ... (truncated, view full listing at source)