Analytics Engineering & Measurement

Data Platforms & Reliability

Versioned pipelines, observability, and governance that keep metrics trustworthy across teams.

Where the work starts

Run a readiness audit across ingestion, transformation, and data contracts
Introduce modular dbt patterns, CI/CD, and testing that match your team structure
Stand up observability with freshness SLAs, anomaly detection, and stakeholder alerts

Intended outcomes

Recovery workflows and documentation that make platform behaviour inspectable
Shared visibility into freshness, lineage, and quality for analysts and executives
Lower cost of change thanks to reusable models and automated deployment

Tools in context

ClickHouse, dbt, Airflow, Superset, BigQuery, Postgres, Python

Put the next question to work.

Bring the question, the available evidence and the constraints. Speak directly with the practitioner who will do the work.

Talk through an analytics problem