Principal Data Architect

Robots and Pencils•Canada, KS

17h•$170,000 - $240,000•Remote

About The Position

Robots & Pencils is seeking a Principal Data Architect to own its most challenging data architecture work, set technical direction across data platforms, and serve as the senior technical authority on how organizations model, move, govern, and trust data. The role establishes standards for warehouses, data lakes, lakehouse environments, streaming systems, and data foundations for AI/ML at scale. The Principal Data Architect champions data mesh, data product, and governance strategies; makes high-stakes platform decisions; and shapes data foundations for production AI systems. This person partners with leadership and clients on data strategy, mentors experienced engineers and architects, and advances a long-term vision for scalable, secure data that compounds in value over time. The role remains hands-on through prototyping, production coding, and reviewing designs and pull requests with delivery teams, and supports pre-sales by shaping solution approaches, estimates, and proposals. This position can be performed remotely in Canada or the United States. The salary range is $170,000–$240,000 USD in the United States or $190,000–$260,000 CAD in Canada. Eligible employees receive paid time off, medical/dental/vision insurance, and 401(k)/RRSP matching. Employment may be conditional on a background check in accordance with local legislation.

Requirements

Senior-level expertise in data architecture across warehouses, data lakes, lakehouse environments, and streaming systems.
Ability to own complex architecture decisions and set technical direction across data platforms.
Experience with data governance, lineage, data quality, cataloging, access control, and compliance considerations.
Expertise in enterprise data modeling approaches, including dimensional modeling, Data Vault, data mesh, and data products.
Experience designing data foundations for AI/ML, including feature stores, embeddings or vector stores, and RAG or real-time retrieval pipelines.
Ability to architect semantic layers, knowledge graphs, and unstructured data pipelines.
Experience with infrastructure as code, CI/CD, and observability standards for data platforms.
Ability to address performance, scalability, cost, reliability, security, and governance across data assets.
Hands-on capability to prototype, write production code, and review technical designs and pull requests.
Strong communication skills for explaining complex technical concepts to engineering teams and executives.
Ability to partner with clients and senior leadership on data strategy and platform direction.
Ability to mentor experienced engineers and architects and serve as a senior technical decision-maker.
AI-forward approach to daily work, including use of modern AI assistants.

Responsibilities

Set data architecture standards for warehouses, data lakes, and lakehouse environments, including medallion architecture and open table format strategy.
Design data and AI governance, including lineage, quality, cataloging, access control for AI agents, and evaluation data.
Define direction for large-scale batch and streaming data systems and orchestration.
Champion enterprise data modeling strategies across dimensional modeling, Data Vault, data mesh, and data product paradigms.
Own architecture for data foundations supporting AI/ML at scale, including feature stores, embeddings and vector stores, and RAG and real-time retrieval pipelines.
Architect semantic layers, knowledge graphs, and unstructured data pipelines for documents, text, images, and audio to provide AI agents with trusted business context.
Drive infrastructure-as-code, CI/CD, and observability standards for building and operating data platforms.
Optimize performance, scalability, cost, and reliability across the data estate.
Own security, governance, and compliance posture across data assets, including GDPR, HIPAA, SOC2, PII, data residency, and audit readiness.
Prototype, write production code, and review designs and pull requests alongside delivery teams.
Use modern AI assistants such as Claude and Cursor to support high-quality work at pace.
Partner with senior leadership and clients as the principal technical voice on data strategy and platform direction.
Support pre-sales by shaping solution approaches, scope, estimates, and proposals, and participating in client conversations.
Explain complex data architecture concepts clearly to engineering and executive stakeholders.
Align data architecture with enterprise strategy by engaging across engineering, AI, analytics, and product organizations.
Set standards for data architecture documentation, modeling, governance practices, and data contracts across teams.
Define and champion architectural standards and best practices on supported engagements.
Build reusable accelerators, reference architectures, and templates to scale the data practice and accelerate delivery.
Mentor experienced engineers and architects to develop their technical judgment and impact.
Make senior technical decisions on difficult architectural and integration challenges.
Evaluate emerging technologies and recommend tools, frameworks, and patterns to improve architecture.