Cloud data warehouses
Design and improve warehouse structures, source integrations, and environments. Build a foundation that supports analysis and downstream applications without leaving business logic scattered across tools.
Advanced data engineering services
A stronger backbone
for everything data.
Reliable data matters long before an AI project starts. Magnetic North builds and improves the pipelines, warehouses, and models that support reporting, business operations, and AI implementation.
Talk about your data platformEngineering that goes deeper
From source systems
to dependable data.
Focused improvements or a broader platform build, shaped around what your business needs next.
ETL & ELT pipelines
Extract data from business systems, load it reliably, and transform it into usable datasets. Address incremental loading, historical changes, retries, and reconciliation as the use case requires.
Data modeling & transformation
Turn raw source records into understandable models and consistent metrics. Document definitions and relationships so analysts, applications, and AI workflows can work from shared business context.
Orchestration & observability
Coordinate dependent workflows, surface failures, and make data freshness visible. Give your team a clearer way to understand what ran, what changed, and what needs attention.
Data quality & governance
Add checks for the conditions your business depends on. Clarify ownership, track important transformations, and account for access requirements throughout the data flow.
Performance & platform improvement
Investigate slow transformations, recurring failures, and inefficient processing. Prioritize changes based on the operating problem and the tradeoffs for your team.
A useful starting point
Tell us where
the friction is.
You don’t need a full platform redesign to get help. Start with the workflow that keeps breaking, the metric no one trusts, or the integration holding up your next project.
- Recurring manual exports and spreadsheet reconciliation.
- Pipelines that fail quietly or deliver stale data.
- Conflicting definitions across dashboards and teams.
- A data platform that needs to support new AI workloads.
Planning an AI implementation? Start by finding out whether your data can support the outcome you want.
Explore AI data readiness ↗Before we build
Good engineering
starts with context.
What’s the difference between data engineering and AI data readiness?
Data engineering builds and maintains the systems that move, transform, and serve data. AI data readiness looks at whether the information and controls needed for a particular AI use case are in place. The work often overlaps, especially when source systems are disconnected or data quality is inconsistent.
Can you improve an existing warehouse or pipeline?
Yes. The starting point can be an unreliable pipeline, a slow model, unclear business logic, or a warehouse that has become difficult to maintain. We investigate the specific problem and scope changes around its impact.
Do you help with analytics and reporting too?
Yes. Shared data models, consistent metrics, and reliable pipelines support analytics and business reporting as well as AI. The engineering scope can focus on those needs even when no AI project is planned.
How do you choose tools?
We consider your existing stack, team, operating requirements, and budget. The goal is a solution your team can understand and maintain. Tool choices should follow the requirements of the work.
Find your next step
What could better
data make possible?
Tell us what you want AI to do, where your data lives, and what’s getting in the way. We’ll start with a conversation about the right next step.
Your inquiry goes directly to Magnetic North.