dbt Consulting & Development Services

Consolidate Data from Dozens of Source Systems into One Trusted Layer

Most companies pull data from 10–30+ tools: CRMs, ERPs, ad platforms, payment processors, IoT sensors, SaaS APIs. Each one has its own schema, naming conventions, and update cadence. A well-structured dbt project standardizes all of these into a single staging layer, applies consistent business definitions (what counts as "revenue," what counts as "active user"), and produces reporting marts that everyone in the company trusts. No more conflicting numbers between departments.

Replace Manual Excel Reconciliation with Automated, Tested Pipelines

Finance and operations teams often spend hours per day manually matching records across systems: trade data vs. custodian records, invoices vs. purchase orders, shipment logs vs. billing. dbt turns this into a scheduled pipeline that runs matching logic, flags exceptions, and delivers a clean reconciliation report before the team starts their day. Built-in tests catch data gaps and mismatches automatically, so analysts spend their time investigating real issues instead of hunting for missing rows.

Build Regulatory and Compliance Reporting You Can Actually Audit

When regulators ask how a number was calculated, you need a clear answer. dbt's built-in lineage tracking shows exactly which source tables, transformations, and business rules produced each metric. Combined with source freshness monitoring, schema tests, and version-controlled SQL, this gives compliance teams a full audit trail from raw data to final report. Every change to the transformation logic is tracked in Git with a timestamp and author.

Normalize Multi-Platform Marketing and Sales Data

Companies running campaigns across Google Ads, Meta, TikTok, LinkedIn, and programmatic DSPs end up with fragmented performance data: different attribution windows, different metric definitions, different naming conventions for the same campaign. dbt can normalize all of these into a unified model with consistent attribution logic, standardized campaign taxonomies, and a single source of truth that feeds dashboards, attribution models, and budget allocation tools.

Move from Legacy ETL to a Modern, Maintainable Transformation Layer

Organizations stuck on Informatica, Talend, SSIS, or thousands of lines of undocumented stored procedures face a real problem: nobody fully understands what the transformations do, testing is manual or nonexistent, and making changes is risky. Migrating to dbt means every transformation is version-controlled SQL, every model has explicit tests, and the full dependency graph is visible in the DAG. New team members can read the code and understand the pipeline in days instead of months.

Scale Your Data Models Without Scaling Your Warehouse Bill

As data volume grows, naive full-table refreshes become expensive and slow. dbt's incremental materializations process only new or changed records, which can cut warehouse compute costs by 80%+ for high-volume tables. Combined with materialization strategy tuning (views for lightweight lookups, tables for heavy aggregations, ephemeral models for intermediate logic), a well-optimized dbt project keeps query performance high and costs predictable.