Services / Data Engineering
Data is only valuable when it's reliable. We design and build the ingestion, transformation, and modelling systems that turn raw, fragmented, distributed data into a single source of truth your organisation can actually trust.
Capabilities
Batch and streaming pipelines built for reliability, observability, and scale. Ingestion from APIs, relational databases, event streams, flat files, and third-party SaaS platforms — with monitoring, alerting, and lineage from day one.
Modern lakehouse implementations on Databricks, Delta Lake, and Apache Iceberg — combining the flexibility of a data lake with the governance, performance, and queryability of a warehouse.
BigQuery, Snowflake, Redshift — modelled, optimised, and governed. Not just provisioned. The difference between a data warehouse that runs slowly and one that is the analytical backbone of the business lives in how it's modelled.
dbt for transformation, Airbyte or Fivetran for ingestion, Airflow or Prefect for orchestration. The modern data stack — implemented with the engineering discipline that most tutorials leave out.
Automated data quality testing with Great Expectations, dbt tests, or Monte Carlo — at every stage of the pipeline. Anomaly detection, freshness checks, volume monitoring. Your data earns trust, rather than assuming it.
Kafka, Flink, Google Pub/Sub — event-driven architectures and real-time data systems for applications where batch latency is not acceptable. Fraud detection, live analytics, behavioural personalisation.
Use cases
E-commerce company unifying data from seven different platforms into a single Snowflake environment with trusted, attributed metrics.
SaaS startup building their first data infrastructure in preparation for Series A investor due diligence — pipeline, warehouse, and a financial metrics dashboard, delivered in six weeks.
Healthcare analytics company whose pipelines were failing silently and producing incorrect clinical metrics that downstream teams had been trusting for eight months.
Media platform processing over 10 million user behaviour events per day through a real-time Kafka + Flink pipeline feeding personalisation and content recommendation models.
Fintech company replacing a fragile, Excel-based financial reporting process with a governed, automated dbt + BigQuery pipeline that reduced close time from five days to four hours.
Retail chain building a demand forecasting data infrastructure that feeds ML models predicting inventory requirements across 200 SKUs and 40 locations.
Platforms & tools
Every engagement starts with a discovery phase — no obligation. We map your current state and give you a concrete roadmap before you commit to anything.
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