Principal Data Architect — Databricks Enablement

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.

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

• 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