Principal Data Architect — Databricks Enablement

Caylent is an AI-first cloud services company that helps organizations turn ambitious ideas into meaningful business impact. As an AWS Premier Tier Services Partner and a charter member of Anthropic's Claude Partner Network, we combine deep expertise in AWS, artificial intelligence, and Anthropic's Claude platform to help customers modernize their technology, build intelligent products, and move AI from experimentation into production.

Our capabilities span generative and agentic AI, cloud migration and modernization, cloud-native application development, data and analytics, DevOps, managed services, security and compliance, and customer experience transformation. At Caylent, our people always come first.

We are a fully remote global company with employees in Canada, the United States, and Latin America. We celebrate the culture of each of our team members and foster a community of technological curiosity. Come talk to us to learn more about what it means to be a Caylien!

We're looking for a Principal Data Architect with deep Databricks expertise to lead high-visibility data platform engagements for enterprise clients standardizing on Databricks as their unified analytics and AI platform.

This role blends architectural depth with a forward-deployed engineering mentality. You'll help define the guardrails and standards that keep a growing Databricks platform governable at scale, and you'll get hands-on alongside client business and engineering teams to implement their first priority use cases — teaching as you build, so the client can increasingly self-serve. You're equally comfortable designing a semantic layer and sitting next to a client engineer walking them through their first production pipeline.

This role owns the use-case side of Databricks enablement — hands-on delivery with client business and engineering teams, coaching toward self-service, and feeding requirements back to the platform/foundation team — while maintaining architectural awareness of the broader platform.

• 10 years of experience designing and building complex data systems, including experience in these areas:
  • Relational database design, optimization and migration
  • Data modeling for both transactional and analytics systems, including implementation of industry-standard data models
  • BI dashboards and visualizations
  • Data governance and MDM
  • Big data processing using Spark, streaming solutions, and NoSQL
  • Machine learning and MLOps
  • GenAI foundational models, along with the approaches and frameworks used with them
  • DataOps practices (Infrastructure as Code, data testing, data versioning, etc.)
• Deep, hands-on Databricks experience — workspace/persona architecture, Unity Catalog, lakehouse design patterns, job orchestration, and Databricks-native governance and AI/ML tooling.
• Demonstrated forward-deployed or embedded-delivery experience: comfortable building alongside a client team early in an engagement, then handing off to self-service as maturity increases.
• Experience with at least two of: Infrastructure as Code tools (Terraform preferred), CI/CD pipelines and tools, Python for analytics (numpy, pandas, matplotlib, etc.) and automation.
• Strong drive toward standardizing and documenting our approach and solutions
• Excellent written and verbal communication skills; high tolerance for ambiguity

• At least 4 years of experience in the AWS data landscape
• 5+ years of deep, hands-on Databricks experience
• High business acumen — able to translate ambiguous asks from non-technical business stakeholders into scoped technical work, and to communicate trade-offs clearly to both engineers and VP-level leadership

• Prior experience in a "platform + use-case" hub-and-spoke delivery model, including feeding platform feedback loops from hands-on delivery work
• Background in consulting, systems integration, or professional services
• Familiarity with SOW-based, time-and-materials delivery and scope management

• →Define strategic roadmaps and Databricks adoption plans for clients, including platform guardrails, a self-service maturity model, and a use-case prioritization approach, in a consultative capacity
• →Operate with a forward-deployed engineer mentality: embed directly with client business and engineering teams to implement their highest-priority use cases hands-on, then progressively shift them toward self-service as platform capability matures
• →Close the loop between "what clients need" and "what the platform supports" — translate needs surfaced during hands-on use-case delivery into concrete feature requests and guardrail requirements for the platform/foundation team
• →Act as a data engineering SME in pre-sales and scoping conversations, shaping engagement approach and staffing alongside pre-sales teams
• →Coach and upskill client architects, engineers, and business-embedded technologists on Databricks best practices, patterns, and self-service tooling
• →Oversee development of data standards, operating procedures, and semantic/lineage layers that keep a Databricks environment governable as adoption scales across business units
• →Perform technical interviews for Architect and Engineer candidates; provide technical guidance and mentorship across the practice
• →Build trusted relationships with client technical and business leadership, balancing platform governance against business teams' desire for speed and autonomy