Data Platform Engineer

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🕒 vor 8 Tagen

🌵 Arizona, California, +13 weitere Bundesländer – Remote

🌵 Arizona – Remote 🏄 California – Remote ⛰️ Colorado – Remote 🐊 Florida – Remote 🌽 Illinois – Remote 🏖️ New Jersey – Remote 🗽 New York – Remote 🏈 Ohio – Remote 🍂 Massachusetts – Remote 🚗 Michigan – Remote ❄️ Minnesota – Remote 🏰 Missouri – Remote 🔔 Pennsylvania – Remote 🤠 Texas – Remote 🧀 Wisconsin – Remote

💵 €120.000 - €160.000 / Jahr

⏰ Vollzeit

🟡 Mittelstufe

🟠 Senior

🏗️ Plattformingenieur

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Clinician Nexus

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

Gegründet 2016

⚕️ Krankenversicherung

📚 Bildung

☁️ SaaS

💰 Seed Round im 2019-12

Healthcare Insurance • Education • SaaS

Clinician Nexus ist eine Plattform, die Gesundheitssystemen ermöglicht, durch Transparenz und Einblicke in Produktivität, Praxismanagement und klinisches Lernen eine hochwertige klinische Belegschaft aufzubauen. Sie bietet Werkzeuge für die Vergütungs- und Studentenverwaltung und unterstützt über 50. 000 Kliniker und 143. 500 Studenten an mehr als 2. 400 klinischen Standorten. Clinician Nexus ist bestrebt, Gesundheitsorganisationen dabei zu helfen, ihre klinische Belegschaft in jeder Phase ihrer Karriere auszubilden, zu engagieren und zu halten.

Beschreibung

• Collaborate with data platform architects to design and specify platform tooling, services, and integrations • Build data lake and lake house architectures • Build streaming data architectures to support data platform tooling and services • Collaborate with software teams to integrate streaming and batch architectures with software products • Collaborate with data scientists, analysts, and business stakeholders to MVP optimal solutions • Collaborate with data governance teams and data platform architects to build governance into technical solutions • Maintain systems and services that provide transparency and observability into our critical systems • Implement and promote engineering and architectural patterns, perform code reviews, and collaborate in architectural reviews • Collaborate with product team data engineers and architects to MVP data products, maintain data models, and promote data modeling best practice • Provide technical leadership and mentorship to product and data engineers, guiding their growth and professional development and enabling their ability to use platform tooling and services • Lead by example through hands-on contributions to designing, coding, and troubleshooting complex data systems • Identify and address performance bottlenecks and optimization opportunities within data pipelines, databases, and processing frameworks • Optimize data processing workflows to improve efficiency and reduce latency • Lead efforts to diagnose and resolve data-related incidents in a timely manner • Will participate in the grooming of stories • Will be responsible for their own tasking towards the completion of stories • Will mentor junior members of the team and guide junior members on best practice • Will participate in design • Will be responsible for the quality of their own code and will participate in code review of others product • Will be responsible for integrating their own code with the team's DevOps plan and implementation

🎯 Anforderungen

• 5+ years relevant experience • Degree in Computer Science, Software Engineering, Information Systems, Information Technology or a related computer degree or equivalent experience. • Must know and have proficiency in one object or object/functional programing language. Preferably Python • Must know common object and object/functional design patterns. Builder, factory, façade, context, etc. • Must have proficiency with Apache Spark • Must know data lake and lake house design principles, OLTP (Online Transaction Processing) design principles, document data stores, and graph • Must know and understand how to build ELT/ETL patterns in a distributed compute system. Preferably Databricks • Must know and have proficiency with common data quality tooling and the design of configurable systems to front end that tooling • Must know and have proficiency with common systems and data observability tooling • Must know standard practices of the Software Development Lifecycle (SDLC) • Must have proficiency in standard SDLC concepts and tooling including unit and integration testing frameworks like Pytest, IDE features like debugging, testing practices like mocking, CICD tooling like Github actions, build tooling (poetry), GIT • Must have proficiency in creating and using basic Restful or GRPC services • Must have proficiency in message bus and streaming technologies and understand common event stream architectures • Knowledge of the health care industry is a plus

🏖️ Vorteile

• Medical and dental coverage at no premium cost for employees • 401(k) and profit-sharing retirement plans • Flexible spending accounts • Generous paid time off (PTO) • Company paid holidays • Gender-neutral parental leave • Bereavement and pet leave • Continuing education and professional accreditation sponsorship • Life and AD&D insurance • Short- and long-term disability • Employee assistance program • Mental health support program