Data engineering · Phoenix metro
Data engineering in Phoenix, for pipelines people actually trust
TechFabric is a data engineering company in the Phoenix metropolitan area, with the office in Gilbert, Arizona. We build pipelines, lakehouse platforms and the governance around them on Databricks for companies across Phoenix, Scottsdale, Tempe, Mesa and Chandler, with 80 Databricks-certified engineers.
The pipeline is rarely the problem. The problem is that two dashboards disagree, nobody can say which is right, and the person who built it left.
Office
1530 E Williams Field Rd, Ste 201Gilbert, AZ 85295
Phone
+1 (480) 681-6806Serving
Phoenix, Scottsdale, Tempe, Mesa, Chandler
Bench
115+ people, 80 Databricks-certified
Why Phoenix companies work with us
Eighty Databricks-certified engineers
That is an unusual concentration for this metro, and it is checkable. Ask every firm you talk to for the number rather than for the badge, because a partner tier weighs revenue alongside capability and a certification count does not.
Governance from the first pipeline
Unity Catalog grants, lineage and audit designed at the start rather than retrofitted the week somebody asks. Retrofitting governance onto an estate is most of what makes it expensive.
We build what sits on top
Most data firms stop at the table and the dashboard. The question that decides whether a platform gets used is who builds the application, the API and the agent above it, and that is work we have been doing for a decade.
In the metro, not flying in
Gilbert is twenty minutes from Tempe and Chandler and an easy drive from the city. For the parts of a build that go better in a room, we can be in yours.
What data engineering covers here
Lakehouse platform build
Medallion structure, Lakeflow pipelines and the storage layout underneath, designed against how the data is actually queried rather than how it arrives.
Unity Catalog governance
Catalogue and schema design, a grant model expressed through groups so joining a team is what changes access, and lineage that survives a refactor.
Migration off a legacy warehouse
Snowflake, Synapse, Teradata and SQL Server, run through our own accelerator with executable playbooks and a cutover that can be reversed.
Streaming and change data capture
Where the business needs the number now rather than tomorrow, built so a replay rebuilds the view instead of approximating it.
Operational data on Lakebase
The transactional side beside the analytical one, in Postgres inside the lakehouse, with the sync direction and freshness stated rather than assumed.
Cost that somebody can defend
Warehouse sizing, job scheduling and storage layout are the three levers that decide a Databricks bill. We instrument them at build time rather than after the first invoice lands.
Where we have done this work
Named sectors rather than a logo grid. Every engagement referenced here is written up on this site with the client named, because a claim you cannot check is not proof.
Financial services and lending
Origination, refinancing and servicing data where a wrong number is a compliance question. Auto Approve, iLending and SWBC are on this site with the work named.
Sport and media
Audience and event data at scale, including a pipeline stitching 400M+ athlete records across sources with automated retries.
Manufacturing and distribution
Estates that outgrew the systems underneath them, where the data work and the modernisation are the same project.
Consumer products
Commerce data judged on the day the traffic arrives rather than on the average day.
How an engagement runs
01
Two weeks, fixed fee
A Databricks Health Check reads the estate and comes back with a scorecard: what is sound, what is costing more than it returns, and what will break next. You keep the document either way.
02
One workload, end to end
We pick a real pipeline with you and take it all the way to production, because a proof of concept that never carries load teaches nobody anything.
03
Build with your team in the workspace
The work runs in your workspace and your repository. Your engineers are in it from the first week, since the pattern has to be one they can extend.
04
Hand over with the reasoning
The written record of what was decided and why, which is the thing the next platform lead actually needs and almost never gets.
Production software, built from the Phoenix metro.
FAQ
Data engineering in Phoenix: common questions
Do you do data engineering in Phoenix?
Yes. TechFabric is in the Phoenix metropolitan area, at 1530 E Williams Field Rd, Ste 201, Gilbert, AZ 85295, with 80 Databricks-certified engineers inside a team of 115+. We work with companies across Phoenix, Scottsdale, Tempe, Mesa and Chandler.
Do we have to be on Databricks?
No, though it is where we are deepest and where the eighty certifications sit. We build on Azure, AWS and Google Cloud too. What matters more than the platform is whether the data has to be governed and the numbers have to reconcile.
How is this different from hiring data engineers directly?
You get a team that has shipped together rather than a bench of resumes, and you get the application and agent layer above the pipeline from the same people. We do not do body-shop staffing, which is the honest difference and the reason to say it before the first call rather than after.
How does a data engineering engagement start?
Usually with a two-week Databricks Health Check at a fixed fee, which reads the estate and returns a scorecard. It is a complete engagement on its own and the document is yours whether or not you continue.
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