01 — What you get

What we deliver

A single source of truth for metrics, dimensions, and KPIs — no more competing dashboards.

Streaming pipelines for operational use cases, batch for analytical workloads, unified in one architecture.

Data modelled for analysts, not engineers. Your team can answer questions without filing tickets.

Warehouse costs actively managed — partitioning, clustering, and query optimisation built in.

02 — Process

How it works

01

Week 1

Data catalogue, lineage mapping, and a written assessment of gaps and opportunities.

02

Week 2–3

Semantic layer design, dbt model architecture, and streaming vs batch decisions.

03

Week 4–10

Warehouse, pipeline, and semantic layer built test-first with data quality checks at every stage.

04

Ongoing

Data quality monitoring, cost optimisation, and documentation kept current.

03 — Stack

SnowflakeBigQuerydbtKafkaFlinkAirflowDagsterFivetranRedshift

04 — FAQ

Which warehouse do you recommend?+

Snowflake for most enterprise use cases, BigQuery for GCP-native teams, Redshift for AWS. We model the cost difference in the design phase.

How do you handle data quality?+

dbt tests and Great Expectations checks at every layer of the pipeline. Failed quality checks block downstream models.

Can you help with ML data pipelines?+

Yes. Feature engineering, training data versioning, and model input pipelines are a core part of our data infrastructure practice.

05 — Related

Retrieval systems, agents and evaluation harnesses — shipped into real workflows, not demos.

Infrastructure that scales predictably: multi-region, IaC-defined, cost-modelled before launch.

SOC 2, ISO 27001 and pen-test readiness built into the delivery pipeline rather than bolted on.

Tell us about your project. A senior architect will respond within one business day.