As organizations accelerate their adoption of Databricks for analytics, AI, and data engineering, many quickly realize that scaling the platform across business units is far more complex than simply spinning up a workspace. Teams often struggle with fragmented ownership, inconsistent governance, rising cloud costs, unclear access models, and an inability to operationalize best practices at scale.

Infinitive’s Databricks Center of Excellence (CoE) offering solves these challenges by transforming Databricks into a unified, enterprise-grade capability. Our CoE framework brings clarity, governance, and repeatability to the platform, enabling organizations to innovate faster while maintaining security, compliance, and cost efficiency.

Why Organizations Need a Databricks CoE

Databricks provides unmatched flexibility for engineering, analytics, and AI, but without a structured approach, the platform can become difficult to manage and scale. Common challenges include:

• Fragmented teams and unclear ownership
• Rising costs without transparency or accountability
• Security and compliance risks in shared data environments
• Inconsistent access models and user experiences
• Lack of standardized governance for data, AI, and models

These challenges prevent organizations from unlocking Databricks’ full potential. A Databricks CoE creates the structure needed to operate the platform effectively across teams, workloads, and business units.

Our Solution: A Framework for Operational Excellence

Infinitive’s Databricks CoE is built on five foundational pillars that enable organizations to operate Databricks as a mature, enterprise-grade platform. Each pillar addresses a critical component needed to scale the platform while maintaining control, governance, and cost efficiency.

Below is a deeper look into each phase of our solution:

1. CoE Team Structure & Roles: Clear Ownership Across the Platform

A lack of ownership is one of the biggest barriers to Databricks adoption. We begin every CoE engagement by defining a clear and intentional operating model that establishes who owns what, how decisions are made, and how teams collaborate.

We help organizations build a structure with clearly defined responsibilities. As an example that has worked in some organizations:

Platform Team

• Owns workspace deployment, configuration, clusters, and overall platform health.

Data Governance & Security Team

• Manages Unity Catalog, permissions, data entitlements, and audit/compliance requirements.

FinOps / Cost Management Lead

• Tracks and optimizes cloud spend, monitors cluster usage, and drives cost efficiency initiatives.

Data Engineering SMEs

• Builds and maintains re-usable processes for ingestion pipelines, transformations, and data storage to ensure consistency across LOBs.

Data Science & ML/AI SMEs

• Lead AI/ML enablement, Feature Store adoption, and MLOps best practices across LOBs.

Business Unit Champions

• Embedded in LOBs to drive adoption, training, onboarding, and feedback loops.

We deliver role definitions, RACI matrices, operating cadences, communication paths, and decision workflows, ensuring the platform has strong, sustainable ownership from day one.

2. Cost Management & Chargeback: FinOps Built into the Lakehouse

Databricks can deliver tremendous value, but without cost governance, it can also become a large line item in a cloud budget. Our CoE includes a strong FinOps foundation to ensure cost transparency and accountability at scale.

Real-Time Cost Monitoring

We integrate Databricks cost reporting with Azure Cost Management, AWS Cost Explorer, or GCP Billing to give organizations a unified cost view across workspaces and business units.

Usage & Spend Dashboards

We implement detailed dashboards that break down spend by cluster, job, user, project, or Unity Catalog object.

Alerting & Automated Controls

Automated alerts detect:

• Runaway clusters

• Inefficient queries

• Misconfigured jobs

• Unexpected spikes in usage

Chargeback/Showback Models

We define usage-based cost attribution models for business units, departments, and projects, driving financial accountability throughout the platform.

Optimization Playbooks

We develop guidance and automated best practices around:

• Cluster right-sizing

• Spot/low-priority compute

• Autoscaling

• Idle cluster policies

• Job scheduling efficiencies

FinOps becomes a natural part of how teams use Databricks, not an afterthought.

3. Security & Platform Administration: Defense-in-Depth for Databricks

Security is foundational. Databricks powers mission-critical and often highly regulated workloads, which means the platform must align with enterprise-grade controls.

We build a defense-in-depth strategy that includes:

Role-Based Access Control (RBAC) via Unity Catalog

Unified governance across data, AI, and compute.

Clear Administrative Roles

• Platform Admins manage workspaces, clusters, and jobs.

• Data Stewards manage catalog-level access and data entitlements.

• Security Admins manage audit logs, compliance reporting, and SIEM/SOC integrations.

Identity Integration (SSO/MFA)

Integration with Entra ID, Okta, Ping, and other identity providers ensures compliance with enterprise authentication standards.

Comprehensive Auditability

• Splunk

• ELK

• Azure Log Analytics

This ensures Databricks fits naturally into your organization’s security posture, not as an exception, but as a fully governed platform.

4. User Access Strategy: Tiered Enablement with Guardrails

A successful CoE must strike the right balance between access and control. We develop tiered user access strategies so every persona gets the tools they need, with appropriate guardrails. For example:

Developer / Data Engineer Access

• Full IDE access, compute control, and development capabilities with managed guardrails.

Data Analyst Access

• SQL-only interface

• Governed datasets

• Dashboards

• No cluster management required

ML / AI Practitioner Access

• MLflow

• Feature Store

• GPU-enabled clusters

• Experiment tracking and reproducibility

BI Consumers

• Read-only governed access via Power BI or other downstream analytics tools.

We implement group-based onboarding, workspace templates, and self-service provisioning patterns that reduce overhead while maintaining governance.

5. Governance Framework: Data, Models, Processes & Compliance

The final pillar brings everything together: a comprehensive governance framework that ensures Databricks is secure, compliant, and manageable over time.

Data Governance via Unity Catalog

• Centralized discovery with lineage, tagging, and classification

• Column-, row-, and tag-level data masking

• Lifecycle policies for retention and archival

Model Governance / MLOps

• MLflow model registration

• Approval workflows

• Versioning and rollback standards

• Promotion processes from dev → test → prod

Process & Policy Governance

• Engineering best practices (naming conventions, CI/CD, code reviews)

• Data onboarding approval workflows

• Ticketing and incident escalation procedures

Compliance & Regulatory Alignment

• NIST

• ISO 27001

• HIPAA

• GDPR

• Enterprise GRC integration

With this governance framework in place, innovation becomes fast, safe, and fully auditable.

What’s Included in Infinitive’s Databricks CoE Offering

Our CoE is not just a framework, it’s a full-service engagement built to accelerate adoption and establish long-term operational excellence. Every engagement includes:

1. Discovery & Current State Assessment

We evaluate your existing Databricks environment, organizational structure, governance practices, and cost posture to establish a baseline for improvement.

2. CoE Blueprint

We deliver a complete operating model, including:

• Team structure and RACI

• Governance model

• Access strategy

• FinOps processes

• Security and compliance guardrails

• Decision-making workflows

This blueprint becomes the foundation of the organization’s Databricks operating model.

3. Implementation

We implement the tools, dashboards, automation, and governance guardrails required to operationalize the CoE. This includes:

• Usage and cost dashboards

• Access and workspace templates

• Guardrails for clusters, jobs, and pipelines

• Data and model governance policies

• Audit and monitoring integrations

4. Enablement

We provide hands-on support for platform teams and business units, including:

• Training sessions

• Playbooks and SOPs

• Office hours

• Persona-based onboarding

Enablement ensures long-term adoption and continuity.

5. Continuous Improvement

The CoE is a living capability. We offer:

• Quarterly reviews

• Governance refreshes

• Cost optimization cycles

• Support for new workspace onboardings

• Updates for new Databricks features and best practices

This ensures the platform evolves as Databricks and your business evolve.

Outcomes: Turning Databricks into an Enterprise Capability

By implementing a Databricks CoE, organizations achieve:

• Operational Clarity – Clear roles and responsibilities across IT and the business.

• Cost Transparency – Real-time visibility and governance over cloud spend.

• Enterprise-Grade Security – Compliance-ready access and audit frameworks.

• Accelerated Adoption – Faster onboarding of users, LOBs, and new workloads.

• Governed Innovation – AI and analytics at scale, with guardrails and consistency.

A Databricks CoE elevates the platform from a powerful tool to a strategic enterprise asset.

Build a Databricks Platform That Scales

Databricks provides the technology, but without the right structure, it becomes difficult to manage, govern, and scale. Infinitive’s Databricks Center of Excellence gives organizations the foundation they need to operate safely, efficiently, and confidently across teams and business units. If your organization is ready to align governance, FinOps, security, and user enablement under a unified framework, Infinitive can help.

Ready to transform Databricks into a true enterprise capability?