Data Engineering & Infrastructure

We default to Apache Airflow or Prefect for orchestration and dbt for transformations because they are open, well-supported, and portable across clouds. That said, we are platform-agnostic — if you are standardised on Databricks Workflows, Microsoft Fabric, or AWS Step Functions, we will work within that stack rather than force a replacement.

Yes. Our standard approach is to run the new pipeline in parallel with the legacy one, reconcile outputs row-by-row, and only cut over once we have verified data parity across a full business cycle. Critical workloads are migrated behind a feature flag so we can roll back instantly if anything drifts.

We build automated quality checks (schema, freshness, volume, distribution, referential integrity) directly into the pipeline using tools like Great Expectations or dbt tests. Ownership is defined during design — we document SLAs per dataset and hand over runbooks so your team knows exactly what to do when an alert fires.

No. We have experience with hybrid and on-prem deployments, particularly where data residency or compliance constraints apply. We will help you quantify the trade-offs honestly rather than push a cloud migration for its own sake.

Cost is a first-class design concern. We partition and cluster tables appropriately, use incremental models, right-size warehouses, and set up query and spend monitoring from day one. For Snowflake/BigQuery we typically reduce cost by 20–40% versus unoptimised baselines within the first few weeks.

Yes — that is an explicit success criterion. We pair with your engineers throughout Build, deliver architecture docs and runbooks, and provide training on the orchestration and transformation layers. We also offer an optional embedded support model for the first few months post-launch.