Services › Data engineering
Data pipelines that refresh on time, every time
We move data from your CRM, ERP, POS and APIs into one place, transform it with tested models, and tell you when something breaks.
Problems we fix
What we do
Source integration
Connectors and custom Python for CRMs, ERPs, POS systems and APIs.
Transformations with dbt
Tested, documented models with clear lineage.
Scheduling and orchestration
Jobs that run in order and retry when they should.
Monitoring and alerts
Checks for freshness and volume so failures are caught early.
Cost review
Right-size connectors and compute so you stop paying for idle syncs.
Hand-off and docs
Runbooks your team can follow without us.
Four steps, no surprises
Discover
A free call to understand your goals, data and tools. You get a written scope and a fixed price.
Design
We agree on metrics and layouts with you before building, so there are no surprises.
Build
Regular updates and working previews, with revisions before sign-off.
Hand off
Documentation and a walkthrough so your team can own what we built.
Recent work and client reviews
Client names are kept private.
Staffing and utilization analytics built on a cloud warehouse with a self-serve dashboard.
BigQueryLooker StudioETLFinancial and sales dashboards on Microsoft Dynamics 365 data and Fabric models.
Power BIDynamics 365FabricThe team built out a full flow for us from CSV files to Google Sheets to BigQuery to a Looker dashboard. Work was done efficiently and well. Project completed successfully, and we are satisfied with the results.
Streamlined our reporting end to end, from pipeline to dashboard. Clarity, speed, and technical depth made the whole thing easy.
Fixed price or monthly
You get a written scope and price after a free consultation.
Data engineering and ETL questions
Which tools do you use for pipelines?
Python, dbt, Fivetran, Airbyte and AWS Glue, chosen by your sources, budget and team. We do not push one stack.
Can you connect to our POS or ERP?
Usually. We use a standard connector where one exists and custom Python against the API where it does not.
How do you catch pipeline failures?
We add freshness and row-count checks with alerts, so a failed job shows up before someone opens a report.
Do we need a data warehouse first?
Not always. For small projects we can load into a database you already have. For growing data we recommend a warehouse.
How long does a pipeline project take?
Adding a few sources with tested models often takes 2 to 4 weeks. These are estimates; we confirm after a call.
Will our team be able to maintain it?
Yes. We document every pipeline and walk your team through it at hand-off.
Related services
Talk to us about data engineering and ETL
A free consultation. You get a written scope and a fixed price before any work starts.