Data Engineering Architect

Emploi pas sur LinkedIn

🕒 il y a 4 mois

🌐 États-Unis, Canada – Distant

🇺🇸 États-Unis – Télétravail 🇨🇦 Canada – Télétravail

🏄 California – Distant

🏄 California – Remote

💵 $210 000 - $260 000 / an

⏰ Temps Plein

🟠 Senior

🔴 Expert

🚰 Ingénieur Data

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Juniper Square

Site WebLinkedInTous les Emplois

201 - 500 employés

💸 Finance

🏠 Immobilier

☁️ SaaS

💰 €75 000 000 Series C en 2019-11

Finance • Real Estate • SaaS

Juniper Square est une entreprise qui propose une plateforme complète et des solutions adaptées aux partenariats d'investissement privés. Fondée en 2014, l'entreprise se concentre sur la facilitation d'une connexion et d'une communication fluides entre les associés commandités (GPs) et les associés commanditaires (LPs) tout au long du cycle de vie de l'investissement. La technologie de Juniper Square est spécialement conçue pour soutenir les entreprises de l'immobilier commercial, du capital-investissement et des sociétés de capital-risque de toutes tailles. La plateforme offre des services tels que l'administration de fonds, la collecte de fonds, la gestion des investisseurs, la conformité et le reporting aux investisseurs, le tout visant à améliorer la transparence, la gouvernance des données et l'expérience globale des investisseurs.

Description

• Define and own the end-to-end data and analytics architecture strategy • Design scalable batch, streaming, and real-time data systems • Establish standards for data modeling, semantic layers, and reporting • Lead architecture reviews and technical decision-making • Drive adoption of modern architectures (lakehouse, data mesh, real-time analytics) • Design and prototype critical data platform components • Write production-quality code for complex or high-impact areas • Review schemas, transformations, dashboards, and analytics models • Troubleshoot performance and reliability issues across pipelines and queries • Optimize workloads for latency, concurrency, and cost • Design and architect a scalable data platform supporting ingestion, transformation, and delivery of both structured and unstructured data across batch and real-time pipelines. • Design a "Data for Agents" strategy, ensuring our data warehouse is structured with the semantic layers and metadata necessary for LLMs to navigate it accurately. • Build AI-ready data infrastructure, including vector stores, embedding pipelines, and retrieval systems that power LLM and agentic workflows. • Develop a RAG-ready data architecture that enables trusted enterprise data retrieval with strong lineage, governance, security, and observability. • Create curated data products and reusable APIs that make high-quality datasets easily consumable by applications, analytics platforms, and AI agents. • Enable self-service data access for engineering, analytics, and business teams through standardized models, semantic layers, and platform capabilities. • Partner with AI, product, and engineering teams to support training datasets, feature stores, and production AI inference pipelines. • Build agentic ETL/ELT pipelines that use AI agents to autonomously discover sources and generate transformations. • Ensure reliability, scalability, and resilience of the platform, including high availability, monitoring, and disaster recovery readiness. • Partner with product, finance, business operations, and leadership teams to define analytics needs • Design scalable data models for reporting and advanced analytics • Ensure analytics solutions are performant, trustworthy, and easy to use • Drive adoption of data-driven culture through reliable insights • Define data governance, lineage, cataloging, and metadata standards • Establish data quality frameworks and validation processes • Ensure privacy, compliance, and secure access to sensitive data • Implement role-based access controls and auditability • Mentor senior engineers, analytics engineers, and data scientists • Partner with product, ML, platform, and business teams • Translate business questions into scalable data solutions • Influence roadmaps using data platform and analytics considerations • Act as the executive technical authority for data and analytics • Define SLAs/SLOs for data availability, freshness, and accuracy • Establish monitoring, alerting, and incident response processes • Optimize cloud costs and query performance • Support capacity planning for data growth • Be an evangelist for pragmatic AI adoption. • Help establish a culture of outcome-driven innovation.

🎯 Exigences

• Advanced degree in Computer Science, Engineering, or related field • 10+ years in data engineering, analytics engineering, or data platform roles • Proven experience architecting large-scale data and analytics systems • Strong hands-on experience with modern data stacks in cloud environments • Deep expertise in data modeling for analytics (dimensional, star/snowflake, Data Vault, etc.) • Advanced SQL skills and proficiency in Python, Scala, or Java • Advanced expertise in dimensional data modeling and semantic layers (e.g., dbt, Cube) to provide "agent-readable" context. • Experience with distributed processing frameworks (Spark, Flink, etc.) • Experience building reporting and BI solutions at scale • Strong understanding of both batch and real-time architectures • Hands-on experience with AWS, Azure, or GCP data services • Experience with BI tools (e.g., Looker, Tableau, Power BI, etc.) • Strong understanding of data governance and security best practices • Ability to operate at both executive and deeply technical levels.

🏖️ Avantages

• Health, dental, and vision care for you and your family • Life insurance • Mental wellness coverage • Fertility and growing family support • Flex Time Off in addition to company paid holidays • Paid family leave, medical leave, and bereavement leave policies • Retirement saving plans • Allowance to customize your work and technology setup at home • Annual professional development stipend