Data Engineering & Enterprise Analytics Consulting
Data pipelines, lakehouse modernization on Databricks and Snowflake, governed data products and predictive analytics — the trusted data layer enterprise AI depends on.
Talk to AX3What the Data & Analytics practice covers
Data engineering and analytics that turn raw data into governed, usable assets.
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| Capability | What it covers |
|---|---|
| Data engineering & pipelines | ETL/ELT pipelines built for scale, reliability and low latency |
| Predictive analytics & forecasting | Demand forecasting, churn and risk models tied to business KPIs |
| Data governance & quality | Lineage, cataloging and quality controls for regulated data |
| Vector databases & embeddings | Foundations for semantic search and RAG-based applications |
| Data platform modernization | Migration to modern lakehouse platforms (Databricks, Snowflake) |
| Analytics & decision intelligence | Reporting and metrics embedded in the workflows where decisions get made |
Swipe the table sideways to see all columns.
Part of AX3 Technology Solutions.
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The same engineers AX3 deploys on Data & Analytics programs are available on contract, contract-to-hire, permanent and remote-team terms.
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Data & Analytics, answered
How is Data & Analytics different from the AX3 AI & Data practice?
This practice builds the data layer itself — pipelines, lakehouse platforms, governance and analytics. The AI & Data practice applies agents, models and Data 360 on top of that layer. Most programs use both, and AX3 sequences the data foundation first where trust in the data is the blocker.
Do we need a full lakehouse migration before we can use our data?
No. AX3 usually starts with the specific data products a business decision depends on, proves the pipeline and quality controls there, then migrates the wider estate to Databricks or Snowflake on a schedule that does not stall delivery.
How does AX3 handle data governance in regulated industries?
Lineage, cataloging, access control and automated quality checks are built into the pipelines rather than added later, so every governed data product carries an auditable path back to its source systems.
Let’s scope your Data & Analytics program
Technology delivery, specialized talent, or both — one conversation to start.
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