Principal Data Architect

Caylent

Brasil

Sobre a vaga

A empresa Caylent está contratando para o cargo de Principal Data Architect — Databricks Enablement em: Brasil | LinkedIn

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Principal Data Architect — Databricks Enablement

Caylent

Brasil

Principal Data Architect — Databricks Enablement

Caylent

Brasil

Há 5 dias

35 candidaturas

Veja quem a Caylent contratou para este cargo

Denunciar esta vaga

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!

The Mission

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.

Your Qualifications

Key Responsibilities:

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

Required Technical Qualifications

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 (