Principal Data Platform 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 We are seeking a Principal Data Platform Engineer to define and evolve our real-time and batch data platform built on AWS and Databricks. This role owns the technical vision for how data is ingested, processed, stored, and served as trusted datasets, metrics, and APIs that power products, decisioning systems, machine learning use cases , and operational workflows. As a Principal, you are a technical authority and force multiplier—deeply hands-on while setting architectural direction across streaming systems, lakehouse design, and data-serving layers. You will partner closely with engineering, analytics, and product teams (and occasionally data science partners) to simplify the platform, eliminate legacy patterns, and establish scalable, reliable foundations for real-time analytics and AI-enabled applications . What You Will Do Define and own the end-to-end data platform architecture, spanning streaming ingestion, lakehouse storage, and data/insight serving layers Architect real-time streaming systems using AWS Kinesis and Spark Structured Streaming to support low-latency use cases including model-driven and decisioning workloads Design stream-to-lakehouse convergence patterns that unify real-time and historical data with strong correctness guarantees required for analytics and ML use cases Build and evolve data and metrics APIs that expose curated datasets for downstream applications and analytics including machine learning systems Establish canonical event schemas and data contracts to support event-driven and API-based consumption by analytics and ML workloads Make deep technical decisions across AWS infrastructure (Kinesis, S3, IAM, networking) and Databricks internals (clusters, jobs, Delta Lake, performance tuning) Drive platform modernization, retiring legacy tools and patterns in favor of simpler, lakehouse-first designs Set standards for high-performance SQL and Spark workloads, optimizing for cost, latency, and scale while supporting data freshness and correctness expectations common to ML pipelines Lead complex platform initiatives from architecture through production delivery and ongoing reliability Provide technical leadership and mentorship, shaping best practices for platform design, data quality, and operability Qualifications 10+ years of 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-serving architectures and APIs for analytics and metrics consumed by downstream applications and ML workflows Advanced Python and SQL skills for building scalable, high-performance data pipelines Demonstrated ability to design idempotent, replayable, and observable data platforms at scale suitable for analytics and machine learning use cases ... (truncated, view full listing at source)