Data Engineering & Pipelines
The most damaging data failures are silent. A source system changes a field, the pipeline keeps running, and the numbers stay plausible for a month before anyone notices the trend was wrong.
We engineer pipelines the way software is engineered: version controlled, tested, observable, with contracts against source systems and alerts when reality diverges from the contract.
Platforms we typically deliver this on
Scope of the service
Batch and incremental ingestion
Reliable extraction from ERP, HR, finance, clinical and line-of-business systems, with watermarking and replay.
Change data capture and streaming
Low-latency replication for workloads that genuinely need it — and honest advice about the ones that do not.
Transformation and modelling
Dimensional and wide-table models built in dbt or native platform tooling, with documented business logic.
Data quality gates
Freshness, volume, schema, uniqueness and referential tests that stop bad data before it reaches a consumer.
Orchestration
Dependency-aware scheduling with retry, backfill and clear failure ownership.
Lineage and observability
Column-level lineage and monitoring so impact analysis takes minutes rather than a week.
Where this is used
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Rebuilding student data pipelines for AI workloads
Nightly extracts from the student information system had no tests and no lineage. We rebuilt them with schema contracts, quality gates and column-level lineage so downstream AI work had a trustworthy base.
Outcome — Silent schema breaks surfaced as alerts before they reached reporting.
Near-real-time position reporting
End-of-day batch could not support intraday decisions. We introduced change data capture on the core system with a streaming pipeline into the analytics platform.
Outcome — Intraday positions available without adding load to the transactional system.
Consolidating feeds from legacy systems
Data arrived by SFTP in fixed-width files from systems predating current standards. We built resilient ingestion with validation, quarantine and reconciliation reporting.
Outcome — Legacy feeds integrated without modifying the source systems.
Before you get in touch
Which tools do you use?
Whatever your platform supports — Databricks workflows, Azure Data Factory, dbt, Airflow, Fabric pipelines, native cloud services. We prefer your existing tooling over introducing another dependency.
Can you work with our on-premises systems?
Yes. A large share of our ingestion work is from on-premises Oracle, SQL Server and PeopleSoft into cloud analytics platforms.
Do you hand over the pipelines?
Always. Code lives in your repositories with documentation and runbooks. Some clients then ask us to operate them, but that is a separate choice.
Often engaged alongside this
Data Platform Modernisation
Lakehouse and warehouse builds designed for parallel running, so the business keeps its numbers while the foundation is replaced underneath.
Read moreData & AnalyticsData Migration & Integration
Moving data between platforms with the testing that lets you sign off — row counts, control totals, business-rule validation and a documented rollback.
Read moreManaged ServicesData & Analytics Managed Services
Managed operation of your data platform and analytics estate, with service levels on data freshness and quality, not only on uptime.
Read moreStart with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.