Data Engineering Architect

📋 Description

• • Juniper Square is on a mission to democratize private markets, making assets like commercial real estate, private equity, and venture capital more accessible through digitization. We are building a more efficient, transparent, and inclusive financial ecosystem. If you are passionate about leveraging technology to improve markets and thrive in a values-driven, globally distributed organization, we want to hear from you.
• • This role is for a Data Engineering Architect, a pivotal leadership position focused on transforming our existing data engineering and analytics capabilities into a state-of-the-art, scalable, and product-centric Data Platform organization. You will be instrumental in shaping the future of data at Juniper Square by defining the overarching vision, designing the architecture, establishing the operating model, and creating a clear execution roadmap.
• • Your primary objective will be to transition from a project-based approach to data delivery towards a robust platform that empowers self-service, ensures data reliability and governance, and makes analytics-ready data readily available across the entire company.
• • This is a hands-on leadership opportunity for a technical visionary who is not afraid to get involved in system design, prototype innovative solutions, conduct thorough code reviews, and guide engineering teams through intricate technical challenges.
• • You will be responsible for modernizing our data stack, setting high standards for data platform components, implementing best practices for data reliability and governance, and enabling various teams to build data products efficiently and securely.
• • Beyond platform modernization, you will ensure our data ecosystem consistently delivers high-quality analytics and actionable insights. This involves defining the architecture for the entire data lifecycle, from ingestion and processing to modeling, semantic layers, analytics, and AI/ML enablement, with a steadfast focus on data trustworthiness, accessibility, security, and performance.
• • You will collaborate closely with engineering leadership, product management, analytics teams, and executive stakeholders to ensure our technology strategy is tightly aligned with key business outcomes. A significant part of your role will involve mentoring engineers, fostering a culture of data-driven decision-making, and elevating the team's collective capabilities, processes, and methodologies to operate as a true Data Platform organization.
• • **Architecture & Technical Leadership:** Define and champion the comprehensive data and analytics architecture strategy. Design and implement scalable solutions for batch, streaming, and real-time data systems. Establish robust standards for data modeling, semantic layers, and reporting practices. Lead architecture reviews, drive critical technical decision-making, and champion the adoption of modern architectural paradigms such as lakehouse, data mesh, and real-time analytics.
• • **Hands-On Engineering:** Actively design and prototype core data platform components. Write production-quality code for complex or high-impact areas of the platform. Conduct thorough reviews of schemas, data transformations, dashboards, and analytical models. Proactively troubleshoot performance and reliability issues across data pipelines and queries. Optimize data workloads for improved latency, concurrency, and cost-efficiency.
• • **Data Platform & Pipeline Ownership:** Architect a scalable data platform capable of handling ingestion, transformation, and delivery of both structured and unstructured data across batch and real-time pipelines. Develop a strategic "Data for Agents" approach, structuring our data warehouse with semantic layers and metadata optimized for accurate LLM navigation. Build AI-ready data infrastructure, including vector stores, embedding pipelines, and retrieval systems essential for powering LLM and agentic workflows. Create a RAG-ready data architecture that ensures trusted enterprise data retrieval with robust lineage, governance, security, and observability. Develop curated data products and reusable APIs to facilitate easy consumption of high-quality datasets 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. Develop agentic ETL/ELT pipelines that leverage AI agents for autonomous source discovery and transformation generation. Ensure the platform's reliability, scalability, and resilience, encompassing high availability, comprehensive monitoring, and disaster recovery readiness.
• • **Analytics & Business Intelligence:** Collaborate with product, finance, business operations, and leadership teams to precisely define analytics requirements. Design scalable data models tailored for reporting and advanced analytics. Ensure all analytics solutions are performant, trustworthy, and user-friendly. Drive the adoption of a data-driven culture by providing reliable and insightful data.
• • **Governance, Quality & Security:** Define and implement standards for data governance, lineage tracking, cataloging, and metadata management. Establish comprehensive data quality frameworks and validation processes. Ensure strict adherence to privacy regulations, compliance requirements, and secure access protocols for sensitive data. Implement robust role-based access controls and auditability mechanisms.
• • **Leadership & Collaboration:** Mentor senior engineers, analytics engineers, and data scientists, fostering their growth and technical development. Build strong partnerships with product, ML, platform, and business teams. Translate complex business questions into scalable and effective data solutions. Influence product and engineering roadmaps by incorporating data platform and analytics considerations. Serve as the executive technical authority and trusted advisor for all data and analytics initiatives.
• • **Operational Excellence:** Define Service Level Agreements (SLAs) and Service Level Objectives (SLOs) for data availability, freshness, and accuracy. Establish proactive monitoring, alerting, and incident response processes. Continuously optimize cloud infrastructure costs and query performance. Support capacity planning to accommodate future data growth.
• • **Culture & Enablement:** Act as a key evangelist for the pragmatic adoption of Artificial Intelligence. Help cultivate a culture centered on outcome-driven innovation and continuous improvement.