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Engineering

Data and cloud platforms, engineered — not assembled.

We build the foundations AI depends on: lakehouses with real contracts, pipelines with lineage and quality gates, and cloud platforms your team can operate at 2 a.m. without us. Infrastructure-as-code from day one, tests before handover, runbooks not tribal knowledge.

IaC-first every resource we ship is codified, reviewed, and reproducible
3 layers bronze → silver → gold: raw, cleansed, and business-ready data
0 click-ops — if it can't be redeployed from code, it doesn't ship
Engineering reference stackEngineering reference stack: sources flow into ingestion with contracts, then a bronze-silver-gold lakehouse, then serving layers for BI, applications, and AI agents, all under observability and governance. AnovaCloud data engineering reference stack contracts validate serve SOURCES OLTP · SaaS · streams files · events INGESTION schema enforcement CDC · batch · streaming LAKEHOUSE bronze → silver → gold Delta / Iceberg · quality gates SERVING BI · semantic layer APIs · agent retrieval OBSERVABILITY + GOVERNANCE lineage · data contracts · cost guardrails FIG. 01 — THE ENGINEERING REFERENCE STACK

The stack we build against — adapted to your platforms, never copy-pasted.

What we do

Engineering offerings

01

Lakehouse Engineering

A governed lakehouse on Databricks, Snowflake, or open table formats — with the medallion discipline that keeps it from becoming a swamp.

  • Bronze/silver/gold layering with promotion rules and quality gates
  • Unity Catalog / native governance: lineage, masking, and access as code
  • Cost architecture: storage lifecycles, compute rightsizing, chargeback
Scope a lakehouse build →
02

Data Pipelines & Platform

Batch and streaming pipelines your team can trust — tested, observable, and owned by contract.

  • Declarative pipelines (dbt / Delta Live Tables / native) with CI checks
  • Data contracts between producers and consumers — breakage caught at build time
  • SLA-backed orchestration with alerting that pages the right owner
Fix your pipelines →
03

Cloud Landing Zones & Platform Engineering

AWS foundations and paved roads: landing zones, guardrails, and self-service platforms that let product teams move fast without breaking the org.

  • Multi-account landing zones with SSO, SCPs, and network baselines
  • Golden paths: templated services, environments, and deployment pipelines
  • FinOps instrumentation from day one — budgets, alerts, and ownership
Build the platform →
04

Analytics & Semantic Layer

One definition of every metric — so dashboards, analysts, and AI agents stop disagreeing about revenue.

  • Semantic models with governed metric definitions
  • BI migration and rationalization onto a single serving layer
  • Agent-ready: the same layer that feeds dashboards feeds retrieval
Unify your metrics →

Engagement models

How engineering engages

ModelFormatBest for
Fixed-scope build Blueprint → build phases, fixed price Lakehouse foundations, landing zones, pipeline rebuilds
Embedded pod 2–5 engineers in your rituals Accelerating an internal team through a hard build
Platform partnership Ongoing, quarterly roadmaps Operating and evolving the platform after initial build

Non-negotiables on every build: infrastructure-as-code, automated tests, observability, runbooks, and a handover your team signs off on. If a vendor won't commit to those, keep looking — even if it's not us.

FAQ

Engineering, answered

We're platform-fluent, not platform-captive: Databricks, Snowflake, and open table formats (Delta, Iceberg, Hudi) for data; AWS-first for cloud with infrastructure-as-code throughout. We recommend based on your constraints, team skills, and cost profile.
Alongside. Our engineers embed in your rituals, pair on hard problems, and hand over with runbooks and training. The goal is a team that can run the platform after we leave.
Governance as code: data contracts, schema enforcement, lineage, and quality gates in the pipeline itself — not a review board that meets monthly. Guardrails that run at build time, not meeting time.
It starts with a blueprint (often from an advisory assessment), then fixed-scope build phases: foundation first, then pipelines and serving layers. Each phase ships working, tested, documented infrastructure.
Yes — cost discipline is engineered in, not audited later: budgets, anomaly alerts, rightsizing, and storage lifecycle policies as part of every platform we build.

Start here

Your data platform should be boring — in the best way.

Reliable pipelines, governed data, costs under control. Tell us where it hurts; we'll blueprint the fix.

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