Data Engineering & Analytics

Connected data.
Clearer decisions.

Bring scattered information together, fix unreliable workflows, and build reporting your team can understand and use.

Discuss your data project
Data engineeringReporting & analyticsAutomationArchitecture

What we do

Make your data
useful, every day.

For teams dealing with manual reporting, mismatched numbers, fragile pipelines, or an unclear path forward.

01

Architecture & data reviews

Understand where your data comes from, how it moves, and what’s creating friction. Assess the tools and architecture against the real needs of the business.

A review of the current setup and a prioritized plan for what to keep, simplify, or change.

02

Pipelines & integrations

Bring data from your business systems into dependable, repeatable flows. Fix fragile pipelines and reduce manual movement between tools.

Pipelines and integrations with validation, error handling, and documentation scoped to your systems.

03

Data modeling & foundations

Give your data a structure that reflects how the business works. Create a clear foundation for analysis without adding unnecessary infrastructure.

Documented models and transformations that make data easier to understand and use.

04

Reporting & analytics

Turn scattered spreadsheets and conflicting reports into a clearer view of the business. Define the questions and metrics before building the dashboard.

Reports or dashboards with agreed metric definitions and checks against the underlying data.

05

Automation & ongoing support

Reduce repetitive data work and address the issues that keep coming back. Plan ongoing help around your workflows and operational needs.

Automated workflows or an agreed support scope for maintaining and improving your data setup.

A practical data philosophy

The right level
of complexity.

Choose the tools that fit your business, your team, and the scale of the problem.

Sometimes that means simplifying a workflow. Sometimes it means building a stronger data platform. We begin with the requirements and explain the tradeoffs, so the system remains understandable as it grows.

Lean Data Engineer is our publication on practical data systems. Its consulting services are provided here by Constellation Systems, bringing the same focus on clarity and maintainability to your projects.

Read our thinking at Lean Data Engineer

How we help

From the current state
to a useful next step.

01

Understand the business.

Start with your goals, the people using the system, and what’s working today. Define the problem before choosing the tools.

02

Build with purpose.

Agree on a clear scope, then design and deliver the solution. Review the work together as it takes shape.

03

Make it last.

Document the work, make the handoff clear, and agree on the support you need as your business evolves.

A few useful answers

Before we begin.

Can we start with a review?

Yes. A focused review can help you understand your setup, identify the main issues, and decide what to do next. Implementation can be scoped as a separate engagement.

What if most of our data is in spreadsheets?

That is a useful starting point. We look at how those spreadsheets support the business, identify repetitive work, and recommend a proportionate next step.

Do we need a new data platform?

That depends on your scale, requirements, and existing tools. We assess what you have before recommending a platform change or additional infrastructure.

Can you fix our existing pipelines and reports?

Yes. We can investigate reliability issues, clarify metric definitions, and improve existing workflows. Access, dependencies, and the exact deliverables are agreed during scoping.

Do you offer ongoing support?

Yes. We can agree on ongoing maintenance and improvements after a review or build. The scope defines responsibilities, included work, and costs.

Start a conversation

Let’s make sense of your data.

Tell us what’s manual, unreliable, or harder than it should be. We’ll start there.

Discuss your data project