Data engineering for every stage of growth.
From your first automated pipeline to a platform ready for AI, we build the data foundation your business runs on.You start where you are. We handle what’s next.
Customers
Every data platform is built in the same order.
Most companies already have some of these layers in place, and gaps in others. We find your first weak layer and build up from there. AI sits on top for a reason: it depends on everything underneath.
Built bottom up
What the foundation makes possible
Your data foundation
Source systems · CRM, ERP, product, finance, files
AI & Automation
Forecasting, AI assistants and workflows that run on the foundation below.
Layers 01 to 04 are solid and you have a decision that repeats at volume.
What we build
- Forecasting and scoring models
- AI assistants grounded in governed data
- Access rules that apply to AI exactly as they do to people
Governance travels with the AI. AI-generated SQL is still SQL. It runs under the role of the person asking, so row access and masking policies apply to an agent exactly as they apply to an analyst. No parallel security model to maintain.
Semantic Layer
Metrics defined once, used everywhere.
Dashboards exist but people don’t trust them or don’t use them.
What we build
- Shared definitions for revenue, customers and churn
- Dashboards in Tableau, Power BI, Looker or Sigma
- Verified queries
Version-Controlled Modeling
Business logic lives in tested code, not spreadsheet formulas.
Two teams report different numbers for the same metric, or one person understands the SQL.
What we build
- dbt models with automated tests
- Documentation generated as we build
- A change process so edits don’t break reports
Data Warehouse
One governed place where all your data lives.
Your data is “centralized” in name only, or warehouse costs keep climbing.
What we build
- Snowflake warehouse design and setup
- Environments, access roles and naming standards
- Cost guardrails
Data Pipelines
Data arrives on its own, on schedule, from every system you use.
Someone assembles numbers by hand from exports and spreadsheets every week.
What we build
- Automated ELT from CRM, ERP, product and finance systems
- Incremental syncs
- Alerts when a sync fails, before a dashboard goes wrong
Three stages. One team the whole way.
Start where you are. We’ll get you to the next stage, then help with the one after that.
- Stage 1
Build the Foundation
Your data is spread across tools and spreadsheets. Reports take days. Nobody agrees on the numbers.
- Stage 2
Make It Usable
Your data is in one place, but teams still build reports by hand or don’t trust the dashboards.
- Stage 3
Put AI to Work
Your foundation is solid. Now you want forecasts, automation, or AI grounded in your own data.
Stage 3 isn’t the finish line. We stay on to support, extend and scale what we built, as your external data team or alongside your own.
Need custom software built around your data? Ask us.
Data engineering in production.
70%lower data ingestion costs
80%less manual reporting
Modernizing Data Ingestion and Reducing Integration Costs
GA4 and Magento ingestion moved from Fivetran to self-hosted Airbyte, with 70% lower integration costs and zero downtime at cutover.
Read the case study Stage 1 · Build the FoundationUnifying Marketing Data Across Disparate Platforms
Six advertising and lead platforms consolidated in Snowflake with Airbyte and dbt, making campaign analysis 5x faster.
Read the case study Stage 2 · Make It UsableAutomated Financial Reporting with Trusted Data
Manual spreadsheet reporting replaced with Airbyte, Snowflake and dbt, cutting manual financial data processing by 90%.
Read the case studyWhat clients say
As a small startup with no internal data team, all of our data came from disparate sources that did not easily link together… The DSC team quickly came on board to figure out ways to unify and standardize our data ingestion.
Platforms we build on
DSC is a Snowflake Select Partner and an Airbyte Partner.