Data & Platform
Pipelines that ingest, transform and reconcile at volume — with the verification that proves what went in came out.
01The problem
Data movement is where silent corruption lives. A migration that reports success can still have dropped rows, truncated fields or coerced a type in a way nobody notices until a total is wrong months later. Volume makes it worse: at scale, nobody eyeballs the output.
02How we approach it
Every pipeline carries its own verification. Row counts, duplicate detection, null checks, type compatibility, business-key consistency and total reconciliation run as part of the job, not as a manual afterwards. If the numbers do not agree, the pipeline says so loudly.
03What you get
04How it runs
Real data types, real edge cases, real dirt. Assumptions about source data are where migrations die.
What the destination requires, and what happens to a row that cannot satisfy it.
Deliberate casts and documented rules — never implicit conversion.
Counts and totals compared automatically. Exceptions surfaced, not swallowed.
ETL pipelines ingesting millions of scanned document pages, with I/O optimisation for high-volume digitisation throughput.
05Where we have done this
Tell us what the system has to do and where it currently breaks. You will get a reply from the engineer who would do the work.