Data engineering,
built as a platform.

It's a working decision, driven by workloads, not by category. Warehouse-first architectures on Snowflake, BigQuery or Redshift give the fastest path to trusted analytics; a lakehouse layer on Databricks or open table formats like Iceberg and Delta gets pulled in where ML, unstructured data, cross-engine access or open-format economics genuinely earn it. We design so components can be swapped and open formats can be adopted incrementally, rather than as a one-way door.

By working backwards from decision latency, not from architectural taste. Most workloads are perfectly served by micro-batch on the warehouse; streaming gets pulled in where the business cost of stale data is real, fraud, operational dashboards, personalisation, ops alerting. When both exist we design them against a single semantic model so the streaming view and the batch view of the same metric agree, and we avoid the classic lambda-shaped bug factory.

As first-class engineering, not as a quarterly clean-up. Data contracts between producers and consumers, schemas, semantics, SLAs and ownership, are versioned and tested in CI. Pipelines carry tests, freshness and volume SLAs, and lineage traces every metric back to the source. Incidents are triaged the same way software incidents are, with owners, runbooks and post-mortems, so trust in the data compounds instead of erodes.

As a rolling wave, never as a big-bang. We stand up the target platform, dual-run the two estates against the same sources, reconcile the outputs metric-by-metric, and cut consumers over in scoped batches with a same-day rollback path. Legacy pipelines are retired only after their replacements have passed reconciliation and lived in production long enough to earn trust, which is usually the difference between a migration that lands and one that gets quietly reversed.

Foundational data determines model outcomes more than model choice. Our data engineering practice is designed to serve both classical analytics and AI on the same governed spine, with embeddings, chunking, feature stores, vector-store operations and lineage on retrieval sets treated as first-class engineering artefacts. That way training data, evaluation data and inference-time retrieval share the same contracts, governance and observability as the rest of the estate.

Cost is engineered, measured and attributed, not blamed on the vendor. We instrument warehouse spend, storage, compute and pipeline runtime, attribute it to consumers and workloads, and tune with query optimisation, storage tiering, workload scheduling and materialisation strategies. The output is a platform whose cost curve is understood, negotiable and defensible to finance, instead of a bill that arrives with a surprise every quarter.