Data architecture & Engineering
Engineering with Intent.
Most data systems weren't designed — they accumulated.
We build them properly: structured, stable, and built to last.
We build them properly: structured, stable, and built to last.
The problem
The cost of accidental architecture
As organizations grow, their data systems grow with them — but rarely by design.
New tools get added.
Logic gets copied and modified.
Definitions drift.
Ownership becomes unclear.
The result:
Reports that contradict each other.
Metrics nobody fully trusts.
Engineering teams spending more time fixing than building.
The Method
How we think about Engineering
System Design
01
We define clear data models, ownership boundaries, and structural patterns before a single line of code is written.
A well-designed foundation doesn’t need to be rebuilt every two years.
These platforms support reliable analytics, machine learning, and AI workloads as complexity grows.
A well-designed foundation doesn’t need to be rebuilt every two years.
These platforms support reliable analytics, machine learning, and AI workloads as complexity grows.
Reliable Data Engineering
02
Every transformation is explicit, traceable, and reproducible.
We build deterministic data pipelines with no hidden logic or undocumented assumptions. When a number changes, you know exactly why.
We build deterministic data pipelines with no hidden logic or undocumented assumptions. When a number changes, you know exactly why.
Built to Scale
03
We engineer for where your organization is going— not just where it is today.
Systems that hold their structure as complexity increases, without recurring rebuilds.
Systems that hold their structure as complexity increases, without recurring rebuilds.
services
Three services. One engineering practice.
01 — Architecture & Design
We design modern data systems from the ground up — defining platform structure, domain boundaries, and data models built for long-term operational stability.
Architectures are designed to support advanced analytics, machine learning, and AI systems as organizations evolve.
Specialized expertise in Databricks Lakehouse environments.
Architectures are designed to support advanced analytics, machine learning, and AI systems as organizations evolve.
Specialized expertise in Databricks Lakehouse environments.
02 — Engineering Implementation
We implement what we design.
Transformation logic is consistent, auditable, and built on internal frameworks that reduce risk and eliminate inconsistencies common in ad-hoc engineering.
Transformation logic is consistent, auditable, and built on internal frameworks that reduce risk and eliminate inconsistencies common in ad-hoc engineering.
03 — Modernization & Migration
We transition legacy systems deliberately. No big-bang rewrites.
Systems evolve in structured phases — preserving what works while replacing what doesn't.
Systems evolve in structured phases — preserving what works while replacing what doesn't.
the practice
How we work
BaseUnit is a focused practice, not a staffing operation. Every engagement is led at the senior level, with direct accountability for quality and outcome.
We prioritize precision over speed, structure over trend, and systems that remain stable long after the engagement has ended.
outcome
What changes after BaseUnit
What your organization looks like after we're done
Reports and metrics your team can fully stand behind.
Transformation logic that's fully traceable — anyone can follow the chain.
No more manual reconciliation to get numbers to agree.
Clear ownership of every part of the data system.
A data foundation that grows with your organization, without structural regression.
who is this for
Built for organizations that take their data systems seriously
Organizations moving to modern data platforms like Databricks.
Teams that have outgrown their initial data architecture.
Leadership that needs analytics they can rely on — for decisions and compliance.
Companies that want their data infrastructure to be an asset, not a liability.