What do your Databricks consulting services include?

Lakehouse migrations, Spark pipeline builds, Delta Lake architecture, MLflow discipline for models headed to production, and cost control. One embedded engineer or a full pipeline team, in your hours.

How senior are the engineers you staff on Databricks work?

Data engineers who have run Spark in production, vetted against the rubric on this page. Databricks layers tooling on Spark; an engineer who only knows the tooling learns the hard parts on your bill, so we screen for the fundamentals first.

Can you reduce our DBU spend?

Often. We size jobs from metrics, set cluster policies, and make Photon and spot decisions from data rather than defaults. We will name an expected saving only after seeing your workspace.

Can you move our models out of notebooks and into production?

Yes. MLflow experiment tracking and the model registry wired into deployment, with monitoring after release, so models ship on a pipeline instead of by hand.

Databricks, Snowflake, or plain object storage?

It depends on the workload, and we will say when a lakehouse is the wrong tool. We run engagements on Databricks and Snowflake both, and sometimes the honest answer is object storage and a scheduler.

How do engagements scale?

Start with one embedded data engineer, grow to a full pipeline build if the work demands it. Same team, your business hours, no restart.

Do you work alongside our existing data team?

That is the default model. Our engineers join your standups and your repos as an extension of the team you have, rather than running a parallel project you have to integrate later.

Which clouds and ecosystems do you integrate with?

AWS and Azure for the platform itself, with Snowflake, Kafka, and Airflow in the surrounding pipeline work. If your stack spans warehouses and a lakehouse, we have run that mix before.

Hire Databricks Developer

Hire Databricks engineers for pipelines that reach production

Our data engineers build Spark pipelines, Delta Lake architectures, and ML workflows on Databricks. Senior nearshore engineers, embedded in your team, working your hours.

When to Hire

When Teams Bring in Databricks Developers

Databricks talent is scarce in a specific way. Spark engineers exist. Delta Lake, Unity Catalog, and MLflow experience layered on top is rarer, and the consultancies that have it bill accordingly.

We staff Databricks work with data engineers who have run Spark in production. They join your team in your hours, and the engagement scales from one engineer to a full pipeline build. They work AI-assisted on notebooks and pipeline code, which shortens delivery while the data architecture stays deliberate.

No marketplace profiles to sift. A development company stands behind every engineer we place.

start today

Stalled lakehouse migration

A warehouse-to-Delta move that lost momentum.

Runaway cluster costs

DBU spend past what anyone budgeted.

Models stuck in notebooks

An ML program that needs MLflow discipline to reach production.

Skills and Use Cases

The Skills Your Databricks Project Requires

Databricks is a unified analytics platform built on Apache Spark, enabling data engineering, data science, and machine learning teams to collaborate and derive insights from big data.

Our Databricks Developers always have

Spark pipeline design and performance tuning

Delta Lake architecture and schema management

MLflow experiment tracking and model deployment

Cluster policies and cost optimization

Integrations with AWS, Azure, and Snowflake ecosystems

Where Teams Use Databricks

Lakehouse migrations from traditional warehouses

Bringing runaway cluster costs under control

Moving ML models from notebooks to production with MLflow

Streaming and batch pipelines on Spark

Related Technologies:

Jupyter

TensorFlow

PyTorch

Add a Databricks Consulting Services

arrow_outward

How We Hire

How We Vet Databricks Engineers

Databricks makes it easy to spend money and hard to judge the work. We score for engineers who make it produce.

Spark fundamentals

Partitioning, shuffles, and skew explained from experience

Trusts the platform to fix slow jobs

Delta Lake

Schema evolution, compaction, and time travel used in a real recovery

Has only seen Delta in a demo

Cost awareness

Cluster policies, Photon and spot decisions, jobs sized from metrics

Cannot name what their last optimization saved

MLOps

MLflow tracking and registry wired into deployment

Experiments tracked, deployment still manual

Platform boundaries

Knows when object storage and a scheduler beat a lakehouse

Recommends Databricks for everything

Our favorite filter: How would you cut a Databricks bill by a third without losing a pipeline?

Azumo came in with a dedicated team that quickly grasped our problem and designed and built our data integration solution. They delivered a clearer picture for our business in a timeframe I didn’t think was possible.

Sean Anderson · Chief Operating Officer, Bento for Business

Benefits of Azumo

Why Azumo for Your Software Development

Ship faster with engineers who build with and for AI. We have delivered production ready solutions since 2016.

JP Lorandi, Azumo's CTO wearing a black collared shirt against a white background.

"Our engineers build production AI every day for our clients and our own primitives. That's the difference between a team that's used AI and one that ships it.”

Juan Pablo Lorandi
CTO, Azumo · 25+ years of software architecture experience.
Certified Claude Architect

Build With AI

Engineers develop with AI daily, compressing delivery cycles without cutting corners.

Senior by Default

We hire for seniority and test for it before anyone joins your team.

Scale on Demand

Grow or shrink the team as your roadmap changes — no renegotiation drama.

Time-Zone Aligned

Real-time collaboration across your full working day, from Latin America.

Engagement That Fits

Dedicated team, staff augmentation, or full project build. You pick the model.

Frequently Asked Questions

Q:
What do your Databricks consulting services include?
Lakehouse migrations, Spark pipeline builds, Delta Lake architecture, MLflow discipline for models headed to production, and cost control. One embedded engineer or a full pipeline team, in your hours.
Q:
How senior are the engineers you staff on Databricks work?
Data engineers who have run Spark in production, vetted against the rubric on this page. Databricks layers tooling on Spark; an engineer who only knows the tooling learns the hard parts on your bill, so we screen for the fundamentals first.
Q:
Can you reduce our DBU spend?
Often. We size jobs from metrics, set cluster policies, and make Photon and spot decisions from data rather than defaults. We will name an expected saving only after seeing your workspace.
Q:
Can you move our models out of notebooks and into production?
Yes. MLflow experiment tracking and the model registry wired into deployment, with monitoring after release, so models ship on a pipeline instead of by hand.
Q:
Databricks, Snowflake, or plain object storage?
It depends on the workload, and we will say when a lakehouse is the wrong tool. We run engagements on Databricks and Snowflake both, and sometimes the honest answer is object storage and a scheduler.
Q:
How do engagements scale?
Start with one embedded data engineer, grow to a full pipeline build if the work demands it. Same team, your business hours, no restart.
Q:
Do you work alongside our existing data team?
That is the default model. Our engineers join your standups and your repos as an extension of the team you have, rather than running a parallel project you have to integrate later.
Q:
Which clouds and ecosystems do you integrate with?
AWS and Azure for the platform itself, with Snowflake, Kafka, and Airflow in the surrounding pipeline work. If your stack spans warehouses and a lakehouse, we have run that mix before.