Senior Data Engineer

Job not on LinkedIn

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $145k - $165k / year

⏰ Full Time

🟠 Senior

🚰 Data Engineer

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RxSense

WebsiteLinkedInAll Job Openings

201 - 500 employees

💼 Consulting

📦 Logistics

🏥 Healthcare

💰 Private Equity Round on 2020-05

Consulting • Logistics • Healthcare

RxSense is a HealthTech company dedicated to modernizing pharmacy benefit management. They develop cutting-edge products that optimize claims adjudication and analysis, aiming to lower costs and improve access within the pharmacy ecosystem. RxSense offers solutions for PBMs, pharmacies, pharma manufacturers, payers, and consumers, supporting better outcomes for millions of people daily. Their products include advanced tools like the RxIQ Enterprise adjudication engine, business intelligence analytics platform, and AI-based pricing optimization. They are known for their flagship prescription savings brand, SingleCare, which helps consumers secure affordable prices while benefiting pharmacy partners.

📋 Description

• Design, build, and maintain scalable data pipelines and platform infrastructure on Snowflake • Architect and implement end-to-end data flows from source ingestion through silver and gold layers • Own platform reliability, performance tuning, and ongoing maintenance • Build systems from the ground up; evaluate and refactor existing systems for scale, cost efficiency, and maintainability • Establish engineering standards for version control, CI/CD, testing frameworks, and documentation • Design and maintain Apache Airflow or equivalent orchestration workflows to schedule, monitor, and manage pipelines • Ensure pipeline reliability, observability, and failure recovery through alerting, logging, and automated retries • Coordinate upstream and downstream dependencies with defined SLAs • Design and maintain governed gold-layer data models and metrics in Snowflake using dbt • Establish metric definitions as a single source of truth with ownership and change management • Push transformation and calculation logic upstream into the warehouse • Partner with analytics and BI stakeholders on data engineering and reporting boundaries • Structure and document data models for AI agents and autonomous analytical systems • Build with coding agents and AI-assisted tooling for pipeline development, modeling, documentation, and testing • Set up and manage development environments, repositories, and agentic-system tooling • Help establish team practices for AI-era data infrastructure • Champion data quality, consistency, and trust • Contribute to architecture decisions and identify automation and process-improvement opportunities • Evaluate build-versus-buy decisions and tooling trade-offs

🎯 Requirements

• 5+ years of hands-on experience in data engineering — building pipelines, data models, and platform infrastructure in a data-driven organization • Proven, hands-on production experience building data models and transformations in Snowflake; deep familiarity with Snowflake architecture, performance optimization, and cost management • Hands-on experience with Apache Airflow or an equivalent DAG-based orchestration platform such as Prefect, Dagster, or Mage; ability to design, deploy, and operate complex pipeline workflows • Demonstrated experience using dbt for transformation modeling, testing, and documentation in a production warehouse environment; equivalent semantic layer tooling experience considered • Strong SQL proficiency, including writing, optimizing, and reviewing complex queries across large datasets • Experience building data platforms or systems from scratch • Comfort with platform development and maintenance as a long-term ownership model • Fluency working in the AI era, including building with coding agents, setting up development environments, and using AI tooling as a core productivity multiplier • Familiarity with version control (Git), CI/CD pipelines, automated testing, and code review • Strong communication skills, including translating business requirements into technical data platform decisions • Experience in pharmacy benefits management, healthcare technology, or a related regulated industry preferred • Experience with AWS, Azure, or GCP preferred • Familiarity with data governance frameworks, data contracts, or data mesh principles preferred • Experience with streaming or near-real-time data pipelines preferred • Exposure to semantic/metrics layer tooling preferred • Experience building demo environments or client-facing data products preferred