J
Juniper Square
Senior Staff Software Engineer (Data)
Skills
Google Cloud PlatformSQL QueriesLookerData EngineeringTableauPythonJavaData AnalysisMachine LearningAIScalaAWSdbtCompliancePower BIAzure
2 months ago
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Important information
Contract type:
Permanent
Salary:
$235,000 – $285,000 / year
Location:
United States
Work mode:
Onsite
Published on:
29 August 2026
What they need
- 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- 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
- 10+ years in data engineering, analytics engineering, or data platform roles
- Ability to operate at both executive and deeply technical levels
- Advanced SQL skills and proficiency in Python, Scala, or Java
- Strong hands-on experience with modern data stacks in cloud environments
- Advanced degree in Computer Science, Engineering, or related field
- Hands-on experience with AWS, Azure, or GCP data services
- Proven experience architecting large-scale data and analytics systems
- Experience with distributed processing frameworks (Spark, Flink, etc.)
- Strong understanding of data governance and security best practices
- Strong understanding of both batch and real-time architectures
- Experience building reporting and BI solutions at scale
- Experience with BI tools (e.g., Looker, Tableau, Power BI, etc.)
- Advanced expertise in dimensional data modeling and semantic layers (e.g., dbt, Cube) to provide “agent-readable” context
- Deep expertise in data modeling for analytics (dimensional, star/snowflake, Data Vault, etc.)
- Experience implementing semantic layers or metrics stores
- Experience supporting AI/ML pipelines and feature engineering
- Background in high-growth SaaS or data-intensive organizations
- Familiarity with real-time analytics and event-driven architectures
- Experience with experimentation platforms or product analytics
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