Data Engineering Architect

🕒 Março 12

🏄 California – Remoto

🏄 California – Remote

💵 $210.000 - $260.000 / ano

⏰ Tempo Integral

🟠 Sênior

🔴 Especialista

🚰 Engenheiro de Dados

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Juniper Square

SiteLinkedInTodas as Vagas

201 - 500 funcionários

💸 Finanças

🏠 Imobiliário

☁️ SaaS

💰 $75.000.000 Series C em 2019-11

Finance • Real Estate • SaaS

A Juniper Square é uma empresa que oferece uma plataforma abrangente e soluções personalizadas para parcerias de investimento privado. Fundada em 2014, a empresa se concentra em possibilitar uma conexão e comunicação contínua entre General Partners (GPs) e Limited Partners (LPs) ao longo de todo o ciclo de vida do investimento. A tecnologia da Juniper Square é feita sob medida para apoiar empresas de imóveis comerciais, private equity e capital de risco de todos os tamanhos. A plataforma oferece serviços como administração de fundos, captação de recursos, gestão de investidores, compliance e relatórios para investidores, todos destinados a aprimorar a transparência, a governança de dados e a experiência geral do investidor.

Descrição

• 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.

🎯 Requisitos

• 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.

🏖️ Benefícios

• 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