Microsoft Fabric
Fabric workspaces, OneLake, lakehouses, warehouses, pipelines, semantic models, Power BI, governance, and migration.
Data & Platform Engineering
From raw data to systems your business can run on.
Platform-agnostic data engineering from Boston—Fabric, Databricks, AWS, Azure, lakehouse and warehouse architecture, ERP/CRM integration, modernization, governance, and reporting.
Microsoft Fabric · Databricks · AWS · Azure · ERP/CRM · Lakehouse · Warehouse · Analytics
What we do
Data platform engineering, end to end.
Databricks
Lakehouse architecture, Delta tables, Spark pipelines, orchestration, governance, performance, and migration.
AWS & Azure data
Cloud-native storage, processing, orchestration, integration, monitoring, and the services that fit the client's environment.
ERP & CRM integration
Connect operational systems, define ownership and business rules, and move reliable data into workflows and reporting.
Platform modernization
Replace brittle scripts, scattered exports, and legacy warehouses with observable, tested, governed pipelines and models.
Analytics & reporting
Semantic models, data marts, governed metrics, and reporting built on a foundation the team can extend.
How we work
Scoped, senior, and built to hand over.
01
Assess
We map sources, current pipelines, and where the numbers break. You get a clear architecture and scope before we build.
02
Engineer
We build the lakehouse, pipelines, and reporting layer in Fabric or Databricks — tested, documented, version-controlled.
03
Transfer
Your team receives the architecture, runbooks, and working system. Ongoing administration and optimization remain available where useful.
Outcomes
What you get when it's done.
- One governed lakehouse instead of scattered exports.
- Pipelines that run on schedule and tell you when they don't.
- Reporting leadership trusts — same number, every screen.
- A platform your engineers can extend without us.
Have a data platform or integration problem in mind?
Tell us where the data lives and where it needs to go.