Enterprise dbt Analytics Engineering Services & Support

Maximize the reliability of every dashboard, report, and AI application in your organization by modernizing the layer they all depend on: data transformation. AICG designs, builds, and operationalizes production-grade dbt implementations that turn raw data from POS, ERP, HCM/payroll, SaaS, and operational systems into tested, documented, business-ready data models inside your cloud data warehouse.

dbt has become the standard transformation framework of the modern data stack, bringing software engineering discipline — version control, modular code, automated testing, documentation, and continuous deployment — to analytics. But adopting dbt and succeeding with dbt at enterprise scale are different things. AICG closes that gap with a delivery methodology and our dbt delivery platform.

Evaluating dbt, or scaling an existing implementation? Our team helps you build the architecture and roadmap:

• dbt Core vs. dbt Cloud platform vs. other options
• Environment, warehouse, and orchestration strategy
• Team operating model and skills assessment
• Prioritized backlog of data products tied to measurable business outcomes

End-to-end delivery from project scaffolding to production data marts:

• Warehouse environments and role-based access controls
• Source definitions with freshness SLAs and standardized staging layers
• Kimball-methodology dimensional models or data vault 2.x: conformed dimensions, explicit fact grains, SCD Type 2, and retail 4-4-5/4-5-4 calendars
• Automated testing, generated documentation, and full CI/CD pipelines

Convert legacy transformation logic — stored procedures, ETL-tool workflows, undocumented SQL — into version-controlled, tested dbt models. Every migration begins with a fixed-scope assessment establishing effort, sequencing, and risk before you commit.

AICG's differentiating specialty. We implement the engineering standards that separate production dbt from prototypes:

• Slim CI (or Slimmer CI) building only modified models and their downstream dependents
• Deferred builds that borrow unchanged models from production, cutting CI cost and runtime
• Manifest preservation with bootstrap fallbacks for reliable state comparison
• Model contracts protecting downstream consumers from breaking schema changes
• Tag-based orchestration and selective scheduling
• Source freshness gating so stale data fails loudly instead of propagating silently
• Synchronous warehouse pre-step gating for dependent procedures
• Nightly full builds that continuously validate the entire project

A structured assessment of your existing dbt estate — model architecture, lineage quality, test coverage, CI/CD maturity, warehouse spend, and run performance — delivered as a scored findings report with a prioritized, executable remediation plan.

Engagements are delivered on DBDeux, AICG's dbt delivery platform, compressing time-to-first-mart from months to weeks:

Role-based training for analysts and engineers covering modeling standards, testing strategy, Git and PR workflow, and operational runbooks — ensuring your team owns and evolves the implementation after delivery.

Ongoing operation under SLA:

• Monitoring and incident response
• dbt and package version upgrades
• Performance and warehouse cost tuning
• Continuous model development

Key deliverables can be extended, adjusted, or customized based on your business's needs through requirements gathering and scoping. The baseline deliverables focus on the following key areas:

• dbt implementation strategy and architecture
• Requirements gathering and use-case prioritization
• Production dbt project (staging, intermediate, and mart layers)
• Dimensional data models and conformed dimensions
• Automated testing framework and model contracts
• CI/CD pipelines with Slim CI and deferred builds
• Source freshness monitoring and orchestration gating
• Migration assessment and legacy logic conversion
• dbt health check and optimization report
• Data documentation and lineage catalog
• Warehouse cost and performance optimization
• Technical training and enablement
• Data quality and governance framework
• Analytics and AI-ready data roadmap

Our engagement follows a phased approach: Discovery and Assessment, Solution Design, Implementation and Integration, Deployment and Validation, and Training and Handoff. Contact us to schedule a discovery call and receive a tailored scope proposal.