Data engineering · Phoenix metro
Data engineering in Phoenix, for pipelines people actually trust
Two dashboards disagree, nobody can say which is right, and the person who built the pipeline has left. TechFabric fixes that on Databricks for data teams across Phoenix, Scottsdale, Tempe, Mesa and Chandler, with tested pipelines, governance an auditor can follow, and a backlog that starts to clear. We're headquartered in Gilbert, Arizona, with 80 Databricks-certified engineers.
Office
1530 E Williams Field Rd, Ste 201Gilbert, AZ 85295
Phone
+1 (480) 681-6806Serving
Phoenix, Scottsdale, Tempe, Mesa, Chandler
Bench
115+ engineers, 80 Databricks-certified
Your team ends up with a platform it can run without us, and a cost somebody can defend when finance asks why the bill went up.
AzureTechFabric is a Databricks partner with an award-winning Databricks engineering team, 80 of them Databricks-certified, and a Temporal partner. Where a system reaches past the workspace we build on Azure, AWS and Google Cloud.
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.
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 downtown 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.
Clients we have done this for
Every one of these is written up on this site.
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
We read the workspace's billing and job-run system tables with read-only access, and the scorecard is ranked by severity, with estimated savings and a thirty, sixty and ninety day order.
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. The next platform lead almost never gets one.
Two weeks, fixed fee, and a written plan whether or not you build it with us.
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?
Both are on the table. If you want individual data engineers on a roadmap your team owns, we place them and they work in your workspace from the first week.
If the pipelines themselves are the problem, you get a team that has shipped together, and the application and agent layer above the pipeline from the same people. Say which at the first call.
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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