Data pipelines & ingestion
Batch and streaming pipelines that pull from every source you have, APIs, databases, events, files, and land it clean, tested, and on time. The plumbing your analytics depend on.
Data Engineering & Analytics
From raw data to decisions.
Data is only worth what you do with it. Tarmac builds the pipelines, warehouses, and analytics that turn scattered raw data into trustworthy answers, then goes a step further: predictive models and automation that move you beyond business intelligence, to action.
What we build
The data foundation, and what runs on it
One senior team across data engineering, analytics, and data science. From the first pipeline to the model that acts on it.
Warehouses & lakehouse
Modern data platforms on Snowflake, BigQuery, and Databricks Lakehouse: modeled, governed, and cost-controlled so a single question has a single trustworthy answer.
Business intelligence & dashboards
Metrics, KPIs, and self-serve dashboards your team actually uses. We wire the semantic layer so numbers reconcile across every report and stakeholder.
Predictive models & forecasting
Demand, revenue, and capacity forecasting that puts a number on what happens next, trained on your history and monitored so it stays accurate as the world shifts.
Anomaly detection
Statistical and ML models that flag the outlier before it becomes an incident: fraud, fault, churn, or a metric quietly drifting off course.
Machine learning, in production
Scoring, segmentation, and recommendation models, deployed and observed on a modern MLOps stack, accelerated by our Databricks partnership.
The actionable BI pipeline
How raw data becomes a decision
Every engagement runs the same disciplined path. No step gets skipped, because a shortcut early is a wrong answer later.
- 01
Intake
We connect to your sources and pull raw data into one governed place, with lineage so you always know where a number came from.
- 02
Clean & process
Deduplicate, validate, and standardize. Bad data is caught by automated tests here, not by a stakeholder in a board meeting.
- 03
Statistical tests
We interrogate the data with real statistics, significance, correlation, distribution, so conclusions rest on evidence, not on a hunch.
- 04
Visualize
The result becomes a chart, dashboard, or report designed for the decision it supports, not a wall of numbers nobody reads.
- 05
Deliver insight
We close the loop with a clear recommendation and, where it earns its place, a model that acts on the pattern automatically.
Beyond business intelligence
A dashboard reports the past. We build for what you do next.
Most analytics stops at describing what happened. We keep going: statistical evidence for why, predictive models for what comes next, and automation that acts on the pattern the moment it appears. Insight only counts when it changes a decision.
How it fits together
Data foundation, models, and platform
Data engineering and AI are two teams that reinforce each other. Here is where each one picks up.
This page: the data foundation
Pipelines, warehouses, analytics, and the governed, trustworthy data underneath everything. Get this right and every downstream decision gets sharper.
Applied AI & ML: the models on top
GenAI features, RAG, and autonomous agents live on our AI team. They run on the foundation this team builds, so the two complement rather than overlap.
Explore AI & Applied MLDatabricks: the platform
As a Databricks Consulting Partner we build lakehouse data platforms and run engineering, analytics, and ML in one place.
See our Databricks workQuestions, answered
Data engineering & analytics FAQ
What is the difference between your data engineering and your AI services?
Data engineering is the foundation: pipelines, warehouses, analytics, and clean, governed data. Applied AI is what runs on top of it: GenAI features, RAG, and agents. The two teams complement each other, and most serious AI work only succeeds because the data foundation underneath it is solid.
What does “beyond business intelligence, to action” actually mean?
A dashboard tells you what happened. We take the next step: statistical analysis that explains why, predictive models that say what happens next, and automation that acts on the pattern. Insight is only useful when it changes a decision.
Do we need Databricks to work with you?
No. We are technology-agnostic and work across Snowflake, BigQuery, AWS, Azure, and GCP. That said, we are a Databricks Consulting Partner, and for teams building a lakehouse or unifying engineering, analytics, and ML, it is often the fastest path.
Can you work with the messy data we already have?
Yes. Most engagements start with data that is scattered, inconsistent, or untrusted. Cleaning, validating, and modeling that data into something reliable is the core of the work, and where the first wins usually come from.
How senior is the team?
Senior. Our engineers average 14 years of experience and work in your time zone under the Tarmac 10 quality process. You get data engineers and data scientists who have shipped this before, not juniors learning on your project.
Sitting on data you can’t make decisions with?
Tell us what you are trying to answer. We will bring a senior team that builds the foundation and turns your data into something you can act on.