Senior Staff Software Engineer, Juniper Square

• This is a deeply hands-on leadership role for a technical expert who actively designs systems, prototypes solutions, reviews code, and guides teams through complex challenges
• 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

Desirable

• We are seeking a Data Engineering Architect to lead the transformation of our current data engineering and analytics function into a modern, scalable, product-oriented Data Platform organization
• You will define the vision, architecture, operating model, and execution roadmap required to evolve from project-based data delivery to a platform that enables self-service, reliable, governed, and analytics-ready data across the company
• You will modernize our data stack, establish platform standards, introduce best practices for reliability and governance, and enable teams across the business to build data products efficiently and safely
• In addition to platform transformation, you will ensure the data ecosystem delivers high-quality analytics and actionable insights
• You will define architecture across ingestion, processing, modeling, semantic layers, analytics, and AI/ML enablement, ensuring data is trustworthy, accessible, secure, and performant
• You will work closely with engineering leadership, product teams, analytics, and executive stakeholders to align technology strategy with business outcomes, mentor engineers, and build a data-driven culture
• Success in this role means not only delivering a modern platform, but also elevating the team’s capabilities, processes, and ways of working to operate as a true Data Platform organization
• 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