Data engineering and analytics platform for a United States logistics company

The client is a United States national logistics and freight operator whose business generates enormous volumes of operational data, tracking, routing, pricing and performance, that it had struggled to turn into reliable analytics. Its data infrastructure had grown organically and could no longer keep pace with the business.

The client's data pipelines were unreliable. Pipeline reliability sat at 58%, meaning analytics were frequently late, incomplete or wrong, and the business had learned not to trust the dashboards. Decisions were being made on stale or manual data.

Data quality incidents ran at roughly 26 per month, each requiring manual intervention and eroding trust further.

The client's small internal data team was consumed by firefighting broken pipelines and had no capacity to build the analytics the business actually needed.

Data engineering talent was scarce and expensive in the client's US market, and the role sits awkwardly between software engineering and analytics, making it hard to hire and retain.

Corpshore Mexico proposed a dedicated data engineering pod in Mexico City, on the client's clock, to both stabilise the existing pipelines and build the analytics infrastructure the business needed. Mexico City's deep technical talent pool and the full time-zone overlap made real-time collaboration with the client's analysts possible.

Corpshore proposed treating reliability as the first objective, on the reasoning that analytics no one trusts are worthless regardless of how sophisticated they are, which matched the client's actual problem.

Sixteen engineers in Mexico City: two technical leads, six senior, six mid-level and two junior, dedicated to the account. The team works US Central hours. Stack: Snowflake, dbt, Airflow, Python, AWS, with the client's existing data sources.

Reliability first. The initial focus was making the existing pipelines trustworthy, on the reasoning that reliability precedes sophistication. The business must trust the numbers before it will use them.

Rebuild on a modern stack. Pipelines were migrated to a modern, observable data stack with quality checks and lineage, replacing the organic infrastructure that had become unmaintainable.

Analytics the business needs. With reliability established, the pod built the analytics the business actually required, in collaboration with the client's analysts in real time.

Documented knowledge transfer. Pipelines, models and runbooks are built to a standard the client's own team can operate.

Data pipeline reliability rose from 58% to 96% by month 12, and the business began trusting and using the analytics again.

Data quality incidents fell from roughly 26 per month to 4, freeing the client's internal team from firefighting.

The client built analytics capabilities it had wanted for years but never had the reliable foundation to support.