AI-Ready Data Lakehouse & ETL
One trusted, governed foundation for your dashboards and AI models.
We design and build a modern lakehouse with tested ETL pipelines that turn raw source data into clean, documented, model-ready datasets — engineered for BI, RAG, and machine learning at the same time. Your team sees working outputs inside the first two weeks and owns every component at handoff.
01 — What you leave with
A running platform, not an architecture document
Every deliverable is live in your cloud account, versioned in your repo, and documented for the engineers who take it over.
01Lakehouse architecture, built
Storage, compute, orchestration, and transformation framework stood up in your environment — sized to your volumes and your team’s ownership model.
02Tested 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.
03Governed, model-ready datasets
Domain data models clean enough for analysts and structured consistently enough for feature engineering and RAG retrieval, with lineage and documentation.
04Observability & alerting
Freshness monitors, quality checks, and pipeline alerts wired from day one — because AI workloads have zero tolerance for silent failures upstream.
05Runbooks & 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.
Schedule a scoping callPlatforms and tools we build on
03 — Why MojoTech
Enterprise data engineering, without the vendor incentive
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.
Start here
Start with a 30-minute scoping call
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.
- 30 minutes, with the engineers who’d do the build
- You leave with a scope, a timeline, and a fit answer
- No reseller agreements — the platform call stays yours
04 — Who this is for
Built for teams whose data has outgrown its current stack
A strong fit if you
- Have data spread across warehouses, SaaS exports, and legacy databases
- Need one governed foundation serving both BI and AI/ML workloads
- Have analysts spending more time reconciling numbers than answering questions
- Want an in-house team that owns the platform after handoff
Probably not yet if you
- Haven’t confirmed which use cases the platform needs to support — start with a readiness workshop
- Need source systems connected and migrated first, before any modeling work
- Are looking to staff-augment an existing roadmap rather than design and build one
Data engineering — AI-ready foundations, fully visible