Data engineering · EonTech

Data your teams can actually trust.

We build the pipelines, warehouses, and streaming layers underneath your analytics and AI. Modeled, tested, and governed. You keep the code, the stack, and the cloud account.

Scope your data platform Explore services
  • AWS · GCP · Azure
  • Open formats, no lock-in
  • BI and ML ready

What we build

From raw events to a single source of truth

We treat data as a product, not a side effect. Every layer is modeled, documented, and tested. That discipline is what makes the analytics on top of it dependable.

  • Pipelines: ETL & ELT

    Batch and incremental pipelines that move data reliably. Idempotent by design, so a re-run never double-counts.

  • Warehouses & lakehouses

    Modeled warehouses on Snowflake, BigQuery, or Redshift. Lakehouse layers where open formats matter.

  • Streaming

    Real-time flows on Kafka or managed pub/sub. Late and out-of-order events handled, not dropped.

  • Governance & quality

    Contracts, lineage, and tests at every hop. You see where a number came from and trust it.

The path data takes

Ingest, transform, serve, observe

One arc, end to end. The engineers who model your data also wire the quality checks, so nothing falls between two vendors.

  1. 01

    Ingest

    We connect the sources: databases, events, APIs, files. Schemas captured, contracts agreed up front.

  2. 02

    Transform

    Modeled in clear, version-controlled layers. Tested transformations, documented logic, no hidden SQL.

  3. 03

    Serve

    Curated tables for analytics, BI, and ML. One source of truth, not five conflicting spreadsheets.

  4. 04

    Observe

    Freshness, volume, and quality checks alert before stakeholders do. Lineage makes every fix fast.

Need models on top of this foundation? See AI & ML model development

Cloud-native, your account

Built where you run, owned by you

We deploy into your AWS, GCP, or Azure account, not ours. Infrastructure as code, open table formats, and clear documentation.

When the engagement ends, you hold a platform your own team can operate. No proprietary toolchain, no hostage data, no surprise renewal.

Operationalise models on it

Common questions

What teams ask about their data platform

  • Warehouse, lakehouse, or both?

    We start from your queries and cost profile, not a fixed product. Most teams land on a warehouse for analytics and a lakehouse layer where open formats and large unstructured data matter.

  • How do you keep data trustworthy?

    Data contracts, automated quality tests, and end-to-end lineage. Pipelines fail loud and early, so a bad upstream change never silently corrupts a dashboard.

  • Will this be ready for machine learning?

    Yes. We design feature-grade, well-governed tables from the start. The same data engineering powers your BI and feeds your models without a separate rebuild.

  • Do we own the stack?

    Completely. Open formats, your cloud account, your code and configuration. We avoid proprietary lock-in so you can run and extend everything after handover.

Let's make your data dependable

Tell us the sources and the questions you need answered. We will scope a first pipeline and a clear architecture. Then we start building.

Scope your data platform [email protected]