Senior Data Engineer

🕒 il y a 9 jours

🏄 California, Colorado, +17 états de plus – Distant

🏄 California – Remote ⛰️ Colorado – Remote 🐊 Florida – Remote 🌽 Illinois – Remote 🐎 Kentucky – Remote 🗽 New York – Remote 🌲 North Carolina – Remote 🏈 Ohio – Remote 🌲 Oregon – Remote 🚗 Michigan – Remote ❄️ Minnesota – Remote 🔔 Pennsylvania – Remote 🌴 South Carolina – Remote 🎸 Tennessee – Remote 🤠 Texas – Remote ⛷️ Utah – Remote ⚔️ Virginia – Remote ☕ Washington – Remote 🧀 Wisconsin – Remote

💵 $165 000 - $185 000 / an

⏰ Temps Plein

🟠 Senior

🚰 Ingénieur Data

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Jellyvision

Site WebLinkedInTous les Emplois

201 - 500 employés

🏥 Santé

💼 Conseil

📣 Marketing

💰 Venture Round en 2017-03

Healthcare • Consulting • Marketing

Jellyvision est une entreprise spécialisée dans la fourniture d'une expérience de gestion des avantages hyper-personnalisée pour aider les employés à choisir, utiliser et apprécier leurs avantages. Le produit phare de l'entreprise, ALEX, offre un soutien pour naviguer dans les décisions complexes relatives aux avantages, y compris l'éducation sur Medicare et le soutien à l'inscription. Jellyvision sert les employeurs, les équipes des ressources humaines, les courtiers, les consultants et les partenaires en utilisant l'analytique prédictive et la science comportementale pour offrir une expérience de conseil et de communication sur les avantages sur mesure. Leurs solutions améliorent l'engagement des employés, renforcent la compréhension des avantages fournis par l'employeur et, en fin de compte, conduisent à des effectifs plus heureux et en meilleure santé.

Description

• Build and operate data pipelines • Design and build pipelines that support data movement across systems - ingestion, transformation, compliance, and cross-domain data flows • Own pipeline operations end to end: monitoring, incident resolution, and data quality across both new and inherited workloads • Make sound pipeline design decisions independently while working within the architecture the team has established • Assess and improve existing data systems • Develop working knowledge of how existing infrastructure fits together by mapping data flows, dependencies and performance characteristics • Improve documentation, observability, data quality, and operational standards across the systems you work in • Identify technical debt and reliability risks and bring recommendations grounded in meaningful impact • Build and maintain data infrastructure • Provision and manage the infrastructure your data workloads require, using established IaC practices and team standards • Contribute to shared infrastructure as the platform evolves, building on new foundations as they become available • Maintain and improve the reliability, performance, and cost-efficiency of the infrastructure you own • Contribute across the data platform • Build enough working knowledge of the team's full system footprint to step in when priorities shift or teammates are unavailable • Contribute to platform work as needed — you won't own the new build, but you should be ready to support it when the team needs flexibility

🎯 Exigences

• 6–8+ years of data engineering experience with hands-on ownership of production systems • Strong pipeline design and orchestration skills in a production environment • Advanced SQL: complex transformations, performance tuning, and debugging against a cloud data lake or warehouse • Strong Python: production-grade code, scripting, testing, and debugging • Working knowledge of infrastructure-as-code for provisioning and managing cloud data resources • Familiarity with AWS data infrastructure: S3, IAM, and relevant managed services • Experience inheriting and improving systems you didn’t build - developing working knowledge, identifying risks, and making them better • Clear written communication: you can document a system, a process, or a recommendation so others can act on it independently • Comfortable working across team boundaries with engineering and product on data needs • Experience using AI-assisted development tools (Claude Code, Cursor, Copilot, or similar) to accelerate engineering workflows • Nice to have: • Data science or analytics background — comfortable with model inputs and outputs, statistical concepts, and supporting analytical or decision-support workflows • Experience with dbt or comparable transformation frameworks: reading models, understanding grain and dependencies, writing tests • Experience with managed ELT tools (Fivetran, Stitch, or similar) • Experience in a regulated industry (healthcare, insurance, financial services) with familiarity around compliance-driven data requirements • SaaS platform experience, particularly with multi-tenant data architectures

🏖️ Avantages

• Check out our benefits here!