Data engineering for numbers that must tie out
Pipelines and reporting layers built by someone who has been accountable for the figures they produce. The goal is not another dashboard. It is a source of truth that survives review.
- Source systems
- Ingest
- Validate
- Transform
- Model
- Reporting / application
The problem
Reporting breaks when the same fact lives in three systems with three values. Every report starts with a manual export, every reconciliation depends on one person, and nobody can trace a figure back to its source.
What we build
- Batch and incremental pipelines between operational systems
- Warehouse and reporting-layer design
- Field mapping, transformation rules, and control totals
- Data quality checks that reject and quarantine instead of silently dropping
- Lineage and audit structure for reported figures
- Scheduled refresh with observable failures and safe retries
Systems involved
- PostgreSQL and SQL databases
- Accounting platforms and ERPs
- Microsoft 365 and file stores
- Payment, property, and operational systems
- Power BI and spreadsheet consumers
Controls and validation
Source-to-target reconciliation with control totals, rejected-record handling, and a repeatable rerun path. A successful load is not treated as a correct load. Validation is the deliverable, and it is documented so your team can run it without the author present.
Implementation examples
- A finance data mart that reconciles to the general ledger before a report is built on it
- An incremental pipeline that loads operational activity and quarantines rejected rows for review
- A reporting model with documented definitions, versioned transformations, and a full refresh path
- A migration rehearsal that proves row counts and balances before cutover
What this does not promise: Data work does not remove the need for a source of truth or for owners of that truth. Definitions and approvals stay with the business, and they are documented as part of the build.
Questions buyers ask
Do you replace our reporting tool?
No. Power BI, spreadsheets, or an existing portal stay as the consumption layer. The work is the data underneath: definitions, transformations, quality checks, and reconciliation that make the numbers defensible.
Can you work with the warehouse or database we already have?
Yes. Existing platforms are usually the right foundation. The engagement starts by documenting what exists, where it disagrees with itself, and which changes are worth making before adding anything new.
What happens to records that fail validation?
They are quarantined with the reason and the source row, and routed to an owner. Silent drops are treated as defects. Every rejected record is visible and countable.
Who owns the pipelines after handover?
Ownership is defined in writing for each engagement. Deliverables include the source, transformation definitions, run documentation, and the reconciliation needed to operate the pipeline without dependency on the author.