AI Data Engineering

We Build It. We Run It. You Keep It.

Our engineers build and run your data platform, with AI writing pipeline code and tests alongside them. Your cloud, your repo, your code to keep.

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AI-Powered Data Engineering: AI writes the pipeline code and the tests. Our engineers decide what ships.
They work inside your team, on your backlog, in your repository, and hand the whole platform over when you are ready to run it.

Key points

AI writes pipeline code, transformations, and tests. Our engineers review every line and stay accountable for what reaches production.
Our engineers work inside your team: your standup, your backlog, your repository. Not a statement of work delivered over the wall.
Every pipeline ships with tests. Data quality is part of the build rather than a phase two nobody funds.
Build, run, transfer: we build the platform, run it while your team learns it, and hand it over on a schedule you set.
It runs on open-source DataOps TestGen and DataOps Observability in your own cloud, so there is no proprietary runtime to unpick when the engagement ends.

How it works

1. Scope

We start on the part of your backlog that is actually blocking someone, not on a platform diagram. You get a short written plan naming what we build first, what it depends on, and how we will know it works.

2. Build

Our engineers join your standup and build against your backlog. AI drafts the pipeline code and the tests; our engineers review and own what ships. Each pipeline goes live with tests for freshness, volume, and schema already running.

3. Run

We operate what we built, on call for it, with observability across every stage. Your team runs it alongside us, which is how they learn a platform rather than inherit one.

4. Transfer

Your engineers take on-call for pipelines they have already been operating. The architecture, tests, and runbooks are written down, and the code was in your repository the whole time.

What you keep

Everything. The platform is built in your cloud account, in your repository, on open-source DataOps TestGen and DataOps Observability. No proprietary runtime sits in the middle of your pipelines, so there is nothing to rip out and replace when we go. That is a deliberate constraint on our side: it means the engagement has to end well to end at all.

AI-Powered Data Engineering

AI writes pipeline code, transformations, and the tests that prove them. Our engineers review every line and own what reaches production, which is the part that makes the speed safe to accept.

Pipelines and ingestion

We land your sources on a schedule you can rely on: vendor files, APIs, databases, and the feeds that arrive late and get restated after you already reported the number. Each one carries freshness, volume, and schema tests from the day it goes live.

Warehousing and lakehouse

The models your business actually asks questions of, built on the platform you already bought. Two-stage by design, so the raw landing of data stays separate from the modelling built on top of it.

Data quality and testing

Tests generated by open-source DataOps TestGen, extended with the rules only someone who knows your domain would think to write. They run on every load rather than on an audit schedule.

Observability

Every stage reports what it did, so a failure names the stage that broke instead of starting a search. Runs on open-source DataOps Observability, with agents for Airflow, dbt, Databricks, and the rest of your stack.

Orchestration and automation

Environments, deployments, and the pipelines between them, automated so a change ships the same way every time. Your engineers stop hand-running jobs and start reviewing what ran.

AI context layer

The shape of data an AI tool needs to answer a question correctly: definitions, grain, and how your sources relate to each other. We build it on Snowflake Cortex and Databricks Genie so your own people can ask and trust what comes back.

Migrations

Moving off legacy orchestration, a warehouse you are retiring, or a platform nobody left documentation for. We rebuild it tested, run both sides in parallel until the numbers agree, then cut over.

Meet the DataOps Experts

The people who scope the work deliver it, with decades of experience in data analytics, software engineering, and DataOps transformation.

Christopher Bergh

Co-Founder & CEO

Gil Benghiat

Co-Founder & VP of Product

Eric Estabrooks

Co-Founder & VP of Services

Chip Bloche

VP of Data Engineering

Aarthy Adityan

VP of Software Engineering

Book

Recipes for DataOps Success

The complete guide to an enterprise DataOps transformation, written by the founders of DataKitchen.

Read Now →

Blog

Data Quality: The DataOps Way

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Talk to our team about how DataKitchen services can accelerate your DataOps transformation.

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Frequently Asked Questions

Common questions about AI Data Engineering

What does AI actually do on a data engineering project?

It writes code. AI drafts the pipeline logic, the transformations, and the data quality tests that prove a load was good, which removes most of the typing that used to fill a build. Our engineers review every line, decide what ships, and stay accountable for what runs in production. The speed comes from generation; the safety comes from review and from tests running on every load.

What is DataKitchen's AI data engineering service?

Our data engineers join your team and build the data platform your business runs on: ingestion, models, the warehouse, the tests, and the observability around all of it. They work in your standup against your backlog, in your cloud and your repository. When your team is ready to own the platform, we hand it over.

How is this different from hiring a contractor?

A contractor works to a statement of work and delivers at the end. Our engineers work inside your team on your backlog, which means priorities can change on a Tuesday the way they actually do. They also bring a practice with them: tests on every pipeline, observability across every stage, and an architecture that has run commercial launches.

Do we own the code?

Yes. The platform is built in your cloud account and your repository, on open-source DataOps TestGen and DataOps Observability. There is no proprietary runtime holding the pipelines together, so nothing has to be replaced when the engagement ends.

Which platforms do you build on?

The ones you already bought. Snowflake, Databricks, dbt, Airflow, Azure Data Factory and Synapse, AWS Glue and S3, Fivetran, Power BI, and Tableau are all in regular use. We build on your stack rather than migrating you onto ours, because a platform you cannot staff is not an improvement.

What does the handover actually involve?

Your team operates the platform alongside us before they operate it without us. Handover is a scheduled transition rather than a document drop: your engineers take on-call for pipelines they have already been running, with the architecture, tests, and runbooks written down.

Does data quality testing come as part of the build?

It is part of the build. Tests land with the pipeline that needs them rather than arriving in a later phase, because a later phase competes with the next feature request and loses. If you need quality work across an estate somebody else built, Data Quality as a Service is the offer aimed at that.