AI-Ready Data Lakehouse & ETL
For data and engineering leaders whose stack was built for dashboards: we design and build a modern lakehouse with tested ETL pipelines that turn raw source data into clean, model-ready datasets for BI, RAG, and machine learning. Your team sees working outputs inside the first two weeks and owns every component at handoff.
We reply within one business day.
Clients include Fiserv, Aetna, Shell, and Under Armour
01 — What you leave with
Every deliverable is live in your cloud account, versioned in your repo, and documented for the engineers who take it over.
Lakehouse architecture, built
Storage, compute, orchestration, and transformation framework stood up in your environment — sized to your volumes and your team's ownership model.
Tested ETL pipelines
Ingestion and transformation code with unit tests, data quality expectations, and CI — so a bad upstream change fails the build, not a board dashboard.
Governed, model-ready datasets
Domain data models clean enough for analysts and structured consistently enough for feature engineering and RAG retrieval, with lineage and documentation.
Observability & alerting
Freshness monitors, quality checks, and pipeline alerts wired from day one — because AI workloads have zero tolerance for silent failures upstream.
Runbooks & knowledge transfer
Operational runbooks for every component plus working sessions with your engineers, so nothing we built depends on us being available.
Want to see a real architecture?
Ask on the scoping call and we'll walk you through a redacted lakehouse design and dbt project from a comparable engagement.
Request a scoping callPlatforms and tools we build on
02 — Why now
Most existing stacks were designed backward from a set of reports. Models need something different: consistent structure, enforced quality, and lineage you can audit. Rebuilding for that after the AI work starts costs multiples of designing for it now.
One foundation, two consumers
Analysts get a trusted semantic layer and models get consistently shaped, governed inputs — from the same tested transforms, not two divergent pipelines.
Quality enforced at pipeline time
Expectations run before data lands, so problems surface in CI instead of as a wrong prediction or a number an executive can't reconcile.
Cost you can see
Storage and compute patterns are designed and instrumented up front, so platform spend scales with usage instead of surprising you at renewal.
03 — Why MojoTech
Healthcasts — Healthcare & Life Sciences
Creating value and improved outcomes with aggregated data and AI.
We gathered data from 300K+ healthcare professionals across thousands of patient cases, warehoused it, and trained models on it. An initial GenAI exploration grew into an ongoing engagement to design, develop, and integrate AI features into production software.
300K+
HCPs’ data unified
500K
Consultations
3,500
Patient cases
We are blown away by how much time we save with the automation features developed by MojoTech. The new system allows us to import and visualize our agent’s commission plus transaction data more efficiently, drastically reducing errors and the time it takes to process payroll.
Since 2008
Product and platform engineering for enterprise and regulated industries, in-house and US-based.
Snowflake, Databricks, dbt, AWS
Delivery experience across the modern data stack — which is why the architecture recommendation isn't theoretical.
Built to hand off
Tests, docs, and runbooks are part of the build, not a phase we run out of budget for.
04 — Who this is for
A strong fit if you
Probably not yet if you
Start here
No pitch deck. We ask about your sources, your platform decision, and the analytics and AI workloads you need to support — then tell you what the build would take, what it would cost you in team time, and what we'd need to start.
What happens next
05 — Questions
Pricing is set on the scoping call, once sources, domains, and deliverables are confirmed. You get the scope and timeline in writing before the build starts.
Mainly the number of source systems and data domains in scope, and how much existing pipeline code can be reused.
Everything is built in your cloud account and region, under your access controls, so your existing security and residency policies apply.
Access to source systems, one technical owner for architecture decisions, and the engineers who will own the platform after handoff.
If the use cases the platform needs to support aren’t confirmed yet, we’ll recommend starting with a readiness workshop instead.
Data engineering — AI-ready foundations, fully visible