01
Lakehouse architectures
Snowflake, Databricks, and open lakehouse patterns with governed schemas and cost controls baked in.
Data Engineering Practice
Lakehouse pipelines & metric-store synchronization.
We migrate teams off brittle ETL into streaming lakehouse architectures — with a governed metric layer your dashboards, agents, and executives can all trust.
Practice
Data Engineering Practice
Delivery
Riyadh · London · Lahore
Model
Fixed-scope · Retainer
Timeline
6–16 weeks / phase
01 — What we do
Three pillars that carry the practice.
02
Streaming pipelines
CDC + event ingest with schema evolution, so downstream systems stop breaking on every migration.
03
Metric-store contracts
One definition of revenue, retention, and margin — versioned, tested, and consumed by every surface.
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We ship this practice as a small, opinionated system — running infrastructure, evaluation harnesses, and the rituals that make leadership trust the numbers.
02 — Capabilities
What we build inside this practice.
Every engagement is scoped as a small system, not a slide deck. You get running infrastructure, documentation, and the metric-store hooks to measure it.
Scope an engagement01Snowflake & Databricks migrations
02Change-data-capture ingestion
03dbt-style metric contracts
04Real-time OLAP with sub-second aggregates
05PII tokenization & data masking
06Airflow / Dagster orchestration
07Cost & query-plan optimization
08Semantic layer for AI agents
Reference architecture
How the pieces move.
03 — In production
Numbers from the field.
40×
Faster dashboard load
Pre-aggregated metric store replaced ad-hoc warehouse joins across 220 dashboards.
60%
Warehouse cost cut
Query plan re-architecture and caching tiers on a large Snowflake footprint.
1
Metric definition per KPI
A single governed contract feeding BI, agents, and executive reporting.
Ready to scope this into your stack?
Book a working session with a lead architect.