Lakehouse architecture
Medallion patterns, workspace structure, data domains, table design, notebooks, reusable code, and governed datasets.
Databricks Partner · Data Engineer Professional
Make Databricks easier to govern, operate, and explain.
Gambill Data brings Databricks depth to broader data strategy and architecture decisions. The work connects lakehouse architecture and production engineering to business risk, cost, ownership, governance, and AI readiness—not a predetermined implementation pitch.
Primary specializationDatabricks
Architecture · Unity Catalog · Delta Lake · Workflows · Cost · AI readiness
Capabilities
From useful notebooks to a dependable platform.
Unity Catalog governance
Ownership, access, lineage, stewardship, documentation, and standards that support delivery.
Delta Lake reliability
Table health, quality checks, schema evolution, recovery paths, and pipeline habits that build trust.
Workflows & productionization
Deployment, monitoring, parameterization, testing, support expectations, and Databricks Asset Bundles.
Cost & performance
Compute, workload, storage, scheduling, and roadmap tradeoffs leaders can understand.
AI readiness
Data quality, governance, privacy, lineage, and operating patterns ready for responsible ML and GenAI.
Review ladder
Match the review to the size of the decision.
Each option stays advisory and strategy-call led. The first conversation identifies the narrowest review that will give leadership a credible answer.
01
Databricks Cost & Governance Diagnostic
$8,000 · About 1 week
A narrow read on compute spend, workload design, and immediate Unity Catalog governance pressure.
02
Lakehouse / Unity Catalog Audit
$12,500-$25,000 · 1-3 weeks
A scale-dependent audit of lakehouse architecture, catalog structure, access, workload patterns, and operating controls.
03
Databricks Lakehouse Readiness Review
$25,000-$45,000 · Typically 3-5 weeks
A broader readiness review for a consequential platform, modernization, governance, or AI decision.
Broader named review$25,000-$45,000
A broader readiness review for a consequential platform, modernization, governance, or AI decision. Scope grows with workspace complexity, workloads, Unity Catalog, stakeholder interviews, cost analysis, and roadmap depth.
Use a resource first
Databricks vs. Snowflake scoring matrix
Compare platform tradeoffs using visible business criteria instead of tool preference.