Data Engineering Architect

Juniper Square

11d

About The Position

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. 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. 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.

Requirements

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

Nice To Haves

Experience supporting AI/ML pipelines and feature engineering
Familiarity with real-time analytics and event-driven architectures
Experience implementing semantic layers or metrics stores
Background in high-growth SaaS or data-intensive organizations
Experience with experimentation platforms or product analytics

Responsibilities

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.