dbt turns SQL select statements into tables and views in your warehouse, in the right order, from files kept in Git.
Salesforce Select Partner · 15+ years of team experience · Delivery in the US, Australia and worldwide
dbt data transformation tested and reviewed before it reaches a report.
Our engineers turn warehouse SQL into dbt projects: models in layers, tests and sources, documentation, CI on every pull request and shared metrics in the Semantic Layer, on dbt Core or the dbt platform.
See CI on a pull request
- 15+Years of team experience
- 20+Enterprise implementations
- 60 minFree dbt data transformation review, with a written plan
- 5.0/5Client satisfaction (CSAT)
Proven Success With
5.0/5 CSAT
New to dbt data transformation?
What is dbt data transformation?
Every model can carry tests and documentation, so changes are checked and explained before anyone reports on them.
It runs from the command line with open-source dbt Core, or on the dbt platform, the hosted service formerly called dbt Cloud.
Where you start
Where should our dbt work start?
Most teams begin with transformations spread across scripts and schedulers. Three common paths from there.
TodaySQL scripts, stored procedures and scheduled jobs
- dbt in your repository
Start in code
Models, tests and docs in Git, run from the command line with the dbt VS Code extension for development.
Best for one team on one warehouse.
- The dbt platform
Use the hosted platform
Scheduled jobs, CI, hosted documentation, Studio IDE and the Semantic Layer in one place.
Best when several developers and teams share the project.
- A current dbt version
Upgrade an existing project
Deprecated features removed and the project checked against the newer v2 parser before any move.
Best when you already run dbt on v1.
What we do
What do dbt consulting services include?
From a first project to a platform several teams build on.
01dbt project setupRepository, layers, naming and environments agreed and built.
- Staging, intermediate and mart layers
- Naming and style guide
- Development and production targets
02Migration to dbtStored procedures and SQL scripts rebuilt as tested models.
- Old outputs compared row for row
- Dependencies made explicit
- Old jobs retired one by one
03Tests and data qualityChecks that run with every build.
- Data tests on keys and values
- Unit tests on tricky logic
- Contracts and source freshness
04DocumentationDescriptions and lineage people can read.
- Model and column descriptions
- Owners and exposures
- dbt Catalog or generated docs
05CI and deploymentEvery change built and tested before merge.
- CI on pull requests
- Production jobs and alerts
- Linting and review rules
06Semantic LayerMetrics defined once for every tool.
- Semantic models and metrics
- BI and spreadsheet access
- AI tools through the dbt MCP server
07dbt platform setupThe hosted platform configured for your teams.
- Environments and jobs
- Studio IDE and access
- Git provider connected
08UpgradesProjects moved to current versions.
- Deprecations fixed
- Packages updated
- Results compared before and after
Project structure
How should a dbt project be organized?
The layout we use as a starting point. Each layer has one job, so a model is easy to find and its tests are easy to read.
analytics/
models/staging/
One model per source table
Renamed, typed and lightly cleaned. Sources declared with freshness checks.
models/intermediate/
Joins and business logic
Reusable steps that marts build on.
models/marts/
Tables people report on
Customers, orders and revenue, grouped by team.
Semantic models and metrics
Definitions for the Semantic Layer.
tests/ and macros/
Custom tests
Checks the generic tests do not cover.
Macros
Shared SQL written once.
snapshots/ and seeds/
Snapshots
History kept for records that change in place.
Seeds
Small reference lists kept in Git.
CI on pull requests
What runs when someone opens a dbt pull request?
CI builds only the models that changed and everything downstream of them, in a temporary schema, and defers the rest to production.
dbt build --select state:modified+ --defer --state prod-artifacts
- PASSChanged models and their children built
- PASSunique and not_null tests on primary keys
- PASSUnit tests on the revenue logic
- PASSModel contract on the orders mart
- PASSRelationships test, orders to customers
- HUMAN REVIEWColumn removed that a dashboard uses
Blocks the merge
A failed build or test stops the pull request until it is fixed.
Isolated schema
CI writes to its own schema, so production tables are untouched.
Platform or pipeline
Run as a dbt platform CI job, or from GitHub Actions or another CI tool.
Data platforms
Which data platforms does dbt run on?
dbt runs inside your warehouse, using its compute. We set up the adapter and the environments for the platform you already have.
- 01
Snowflake
Models built as tables, views, incremental tables or dynamic tables.
- 02
Databricks
Models built as Delta tables governed by Unity Catalog.
- 03
BigQuery
Partitioned and clustered tables for large event data.
- 04
Postgres
Smaller warehouses and application databases, on dbt v1.
- 05
GitHub
The project, pull requests and CI checks.
- 06
Salesforce data
CRM data modeled in the warehouse for reports and Data 360.
Semantic Layer
What is the dbt Semantic Layer?
Metrics defined once in the dbt project, powered by MetricFlow, and queried by BI tools, spreadsheets and AI tools. It needs a Starter or Enterprise tier account on the dbt platform.
Mart models
Semantic models
Entities
Dimensions
Metrics
MetricFlow
BI and spreadsheets
AI tools via MCP
Defined in the project
Semantic models describe entities, dimensions and measures on mart models.
Compiled at query time
MetricFlow writes the SQL and joins for each metric request.
Same number everywhere
Every tool asks for the metric by name, so definitions cannot drift.
How it runs
How does a dbt engagement run?
Three stages. The written proposal states the scope and milestones before anything starts.
- Foundation
A project the team can build on.
- Current SQL and jobs reviewed
- Layers, naming and environments agreed
- First domain modeled and tested
- CI on pull requests
- Expand
More domains, same standards.
- Old scripts migrated and compared
- Documentation and owners
- Semantic Layer metrics, if in scope
- Run
Your team owns the project.
- Production jobs and alerts
- Review guide and training
- Monthly plan, if you want one
Cost and timing
What it costs and how long it takes
The review is free
A 60-minute architecture review of your dbt and data transformation setup, with a written plan you keep.
A fixed-scope proposal first
Before any work starts you get a written proposal: a fixed-scope project with milestones, or a monthly plan. You approve it, or you do not.
Timing follows scope
How long it takes depends on what is in scope. The proposal states the milestones, so you see the timeline before you commit.
Client stories
Client work we have delivered
Real engagements, described by industry. Each story covers the starting position, what we built and how we delivered it, end to end.
NonprofitContribution Reporting the Board Can Trust for an Environmental NonprofitSalesforce for an Australian environmental nonprofit: 14 reports and KPI dashboards for financial, in-kind and volunteer contributions, and 553 historical records corrected and reimported.Salesforce Platform · Reports and Dashboards · Data Loader · FlowRead the story 
Financial ServicesSelf-Hosted AI Research Infrastructure Behind a Salesforce Deal EngineSelf-hosted AI agent infrastructure for a US private equity advisory firm: n8n workflows, PostgreSQL, web research and Google Drive mandate documents, deployed with Coolify and writing results back to Salesforce.n8n · PostgreSQL · Coolify · FirecrawlRead the story 
Financial ServicesAI Deal Matching That Screens Companies Against Every Investor MandateSales Cloud, Agentforce, Data 360 and Prompt Builder for a US private equity advisory firm: mandates, AI first-pass ranking, deep scoring with rationale, and deals created from approved matches.Sales Cloud · Agentforce · Data 360 · Prompt BuilderRead the story Client reviews
What clients say about working with our architects
See how teams describe working with SynconAI, from first scope to production delivery.
Salesforce Select Partner30+ Salesforce certificationsDelivery from Delaware & Sydney, worldwide
Simply Outstanding
“SynconAI Consulting are Simply the Best in the Business When it comes to Salesforce implementation and AI-powered solutions, SynconAI are in a league of their own. Their expertise, dedication, and ability to deliver results that truly move the needle sets them apart from every other consulting partner we've worked with. What they achieved with our Sales Cloud, Service Cloud, and custom Agentforce agent has completely transformed how we operate streamlining our pipelines and our customer service, and automating tasks that used to consume hours of our team's time. They don't just implement technology they transform businesses. If you want the best, you work with their team.”
Sales CloudService CloudAgentforce
Jack BennettConsumer Goods & Retail · United States
“I had a very urgent deadline for our project reporting to be delivered and needed to build the module, dashboard and output reports in Salesforce. SynconAI were very responsive - they met with me online, responded to my emails quickly and took calls - whatever was needed to progress the work quickly. We are thrilled with the outcome and will continue to work with SynconAI in the future to continue to build our Salesforce capability and platform.”
Sales CloudService CloudNonprofit Cloud
Salesforce Managed ServicesAustraliaSalesforce Verified
“The team at SynconAI were fantastic, and promptly delivered on each project. They were able to guide us through every stage and made sure we were satisfied with the outcomes on multiple scopes of work.”
Sales CloudSlackFlowAgentforceMuleSoft
ManufacturingAustraliaSalesforce Verified
“They have been great and helping us transform out business by bringing what I conceptualize to life. Great at translating process/workflow needs into a solution. Been a pleasure work with and they're a critical part of our team and will be instrumental into bringing about my vision.”
Sales CloudFinancial Services CloudArchitectureAI
Financial ServicesUnited StatesSalesforce Verified
“Working with SynconAI was a seamless and highly professional experience from start to finish. They took the time to deeply understand our business processes before designing and implementing a Salesforce solution tailored specifically to our operational needs. The team demonstrated strong technical expertise across Salesforce configuration, automation, integrations, reporting, and user experience design.”
SalesforceExperience CloudCustom DevelopmentAutomationReporting
Salesforce ImplementationSalesforce Verified
Frequently asked questions
dbt data transformation, answered.
Straight answers about dbt Core, the dbt platform, tests, CI and the Semantic Layer.
Building warehouse transformations as a dbt project: SQL models in layers, tests, sources, documentation and CI on every pull request, with shared metrics in the Semantic Layer.
Yes. Our engineers set up, migrate and extend dbt projects as a fixed-scope project or on a monthly plan. SynconAI is an independent consulting firm.
Yes. Our analytics engineers can join your team for a migration or a new domain, working in your repository and to your standards.
dbt Labs now calls its hosted product the dbt platform. It includes scheduled jobs, CI, hosted documentation, the Studio IDE, dbt Catalog and the Semantic Layer.
Yes. dbt Core is open source under the Apache 2.0 license. dbt v1 is the original Python-based generation and v2 is the newer Rust-based one.
Snowflake, Databricks, BigQuery and Redshift are generally available on dbt v2. Postgres runs on dbt v1. We check adapter support for your platform first.
Data tests such as unique, not_null, accepted_values and relationships, custom tests, unit tests on model logic, model contracts and source freshness checks.
Yes. Defining and querying Semantic Layer metrics needs a Starter or Enterprise tier dbt platform account. Models, tests and docs work without it.
dbt, dbt Core and dbt Cloud are trademarks of their owner. Snowflake, Databricks, Unity Catalog, BigQuery, PostgreSQL, Redshift and GitHub are trademarks of their owners. Salesforce and Data 360 are trademarks of Salesforce, Inc. SynconAI is an independent consulting firm; apart from its Salesforce partner status, it is not affiliated with or endorsed by these companies.
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