Lakehouse architecture
Medallion patterns, workspace structure, data domains, table design, notebooks, reusable code, and governed datasets.
Databricks Partner · Data Engineer Professional
Databricks consulting for decisions that have to hold up in production.
Work directly with a Databricks consultant who connects lakehouse architecture and production engineering to business risk, cost, ownership, governance, and AI readiness, without forcing every problem into an implementation pitch.
Primary specializationDatabricks
Architecture · Unity Catalog · Delta Lake · Workflows · Cost · AI readiness
At a glance
Know what the engagement is before the first call.
Scope is confirmed on the Strategy Call, but the working boundaries stay visible from the start.
- Best fit
- Leaders making a consequential Databricks cost, governance, architecture, modernization, or AI-readiness decision.
- Review scope
- Workspaces, workloads, compute, jobs, Unity Catalog, access, lineage, Delta patterns, deployment, quality, ownership, and operating controls.
- Deliverables
- Findings, risk register, cost and governance pressure points, architecture recommendations, and a prioritized roadmap.
- Typical timeline
- About 1 week for the focused diagnostic; 1-5 weeks for broader lakehouse and readiness reviews.
- Pricing
- $8,000 for a focused diagnostic; $12,500-$25,000 for a Lakehouse / Unity Catalog Audit; $25,000-$45,000 for a readiness review. Migration leadership is scoped to the estate after an inventory.
- Not included
- Managed operations, unlimited implementation, and vendor-paid recommendations are not included.
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 and productionization
Deployment, monitoring, parameterization, testing, support expectations, and Databricks Asset Bundles.
Cost and 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.
When the decision is made
Move the workloads without breaking the numbers people trust.
Founder-led leadership of a move onto Databricks: target architecture, wave plan, output reconciliation, and cutover decisions, with hands-on implementation support scoped per wave.
Design & sequence
Target architecture, Unity Catalog design, and a wave plan built from the workload and report inventory.
Old and new agree
Reconciliation checks compare legacy and Databricks outputs before any report or downstream consumer moves. Cutover and rollback calls stay with Chris.
Ownership
Hands-on implementation support is scoped per wave alongside your engineers and existing partners, so the platform stays yours after cutover.
Databricks Migration LeadershipScoped to the estate
Phased, one wave at a time. Most migrations start with the Lakehouse Readiness Review or the Modern Platform Blueprint, so the wave plan rests on an inventory instead of a guess. Pricing follows that inventory.
Use a resource first
Databricks vs. Snowflake scoring matrix
Compare platform tradeoffs using visible business criteria instead of tool preference.