Data Engineering Services
We build the data pipelines and models everything else depends on: automated ETL and ELT, a governed cloud warehouse, and version-controlled transformations with tests. Data arrives on schedule, and the numbers survive scrutiny.
Wherever you’re starting
Starting from scratch
No warehouse yet. Data lives in apps and spreadsheets. We stand up ingestion, a warehouse and your first models, flowing within weeks.
Fixing what exists
Pipelines break, costs climb, or the logic lives in one person’s head. We assess, keep what works, and stabilize the rest.
Scaling up
The foundation works, but new sources, teams and AI projects are coming. We extend it without a rebuild.
When to bring in a data engineering team
Manual stitching
Someone spends hours every week assembling numbers by hand from the CRM, the ERP, and a folder of spreadsheets.
Silent failures
Syncs break quietly and you find out from a wrong dashboard, usually in front of the wrong audience.
Logic nobody can touch
Business rules live in spreadsheet formulas and undocumented SQL that one person understands.
Costs creeping up
Pipeline and warehouse spend grows every month and nobody can explain exactly why.
Reliable pipelines, without the babysitting
Every hop is automated, incremental, and monitored. Data moves only when it changes, and problems surface through alerts, not complaints.
Diagram of a modern ELT pipeline: source systems including Postgres, Salesforce, and files flow through automated incremental ingestion into a cloud data warehouse, then into version-controlled and tested dbt models, then out to dashboards, AI, and applications. An observability bar underneath monitors freshness, volume, and failures across every stage.
What our data engineering services include
From first pipeline to a platform your whole organization reports from, built to production standards from day one.
- Automated ELT pipeline development with incremental replication
- ETL modernization: legacy ETL rebuilt as monitored, modern ELT
- Cloud data warehouse design, build, and performance tuning
- Data transformation in dbt, with business logic documented as code
- Version-controlled data models in dbt with automated testing
- Data quality monitoring wired to alerts, not wishful thinking
- Orchestration and scheduling that fails loudly and recovers cleanly
- Cost-aware pipeline design, so spend scales with value
- Documentation generated as a by-product of how the work is built
- Enablement for your internal team, at whatever depth you want
How we work
Map
Inventory sources, consumers, and priorities. Decide what moves first and what can wait.
Stand up
Ingestion and warehouse for the highest-value domains, flowing automatically within weeks.
Model
Business logic as tested, documented code, so the numbers mean the same thing everywhere.
Monitor & extend
Observability, tuning, and new domains as trust builds. This stage never really ends, and that is by design.
Outcomes
70%
reduction in data ingestion costs after we migrated an enterprise customer’s pipelines to Airbyte
Cost efficiency matters as much as capability. The right architecture delivers both.
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 studyDSC developed the ETL pipelines from Braintree, Google Sheets, and QuickBooks into a Snowflake data warehouse… a polished, finished product that updates in real-time as new data comes in.
Questions we hear most
Which tools do you build with?
Typically Airbyte for ingestion, Snowflake for warehousing, and dbt for modeling. We are partners with Snowflake and Airbyte, and we also adapt to platforms you have already committed to. The pattern matters more than the logo.
ETL or ELT: which do you build?
Mostly ELT: data lands in the warehouse first, then gets transformed in dbt, where logic is tested and version-controlled. Where a legacy ETL process still makes sense, we keep it and make it observable.
Do you work with companies that don’t have a data team?
Yes. Many of our clients start with no internal data team at all. We act as your data team end to end, then hand over as much or as little as you want.
Can you fix what we have instead of rebuilding?
Often, yes. We assess first and keep what is sound. Plenty of engagements are targeted: stabilize the pipelines, add testing, and leave working pieces alone.
How long until we see value?
First domains typically flow within weeks. A Snowflake-based analytics foundation can stand up in under two months, depending on scope, with value landing domain by domain rather than at the end.
Who maintains it after launch?
Whatever mix fits you. We can run it as your external data team, enable your internal team to take the lead, or share the load. Most clients land somewhere in between, and that can shift over time.
Where this leads
Make your pipelines boring
Boring means reliable, cost-aware, and quietly on time every morning. Let’s talk about what that would take in your environment.
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