The team at Daphnis Labs redefined what’s possible for Urbanface. They delivered a bespoke, animation-heavy website that remains incredibly quick and functional. The…
Data engineeringfor pipelines,platforms & reporting.
We connect source systems, build data pipelines and organise trusted datasets for analytics, operations and AI.
Databases & APIsFiles & eventsBusiness definitions
Data platform
Reporting tablesOperational feedsAI-ready datasets
Tools & technologies
- Airflow or managed orchestration
- Spark where justified
- SQL transformation
- Cloud warehouses and lakehouses
- Streaming platforms
- OpenLineage-compatible tooling
- Observability
What Can We Build?
Discuss Your BuildETL / ELT Pipelines
Streaming Ingestion
Data Warehouses
Quality Controls
Data Migrations
Analytics Datasets
What You Actually Get
- Source and data-contract discovery
- Batch and streaming pipelines
- Warehouse and lakehouse modelling
- Quality and reconciliation controls
- Lineage, access and observability
- Migration and operating runbooks
- Source and data-contract discovery
- Batch and streaming pipelines
- Warehouse and lakehouse modelling
- Quality and reconciliation controls
- Lineage, access and observability
- Migration and operating runbooks
How We Deliver
Step 1 / 5
Contract
- Contract
- Ingest
- Model
- Validate
- Serve
Choose Your Starting Point
Source Assessment
- Named sources and consumers
- Quality, access and freshness requirements
Pipeline Build
- Agreed sources and datasets
- Batch or streaming delivery needs
Platform Modernisation
- Existing jobs and migration scope
- Parallel validation and controlled cutover
- Founded in
- 2013
- Projects delivered
- 550+
- Client countries
- 43+
- Global offices
- 3
FAQs
When is Data Engineering a good fit?
It fits organisations whose reporting, operations, product or AI work depends on data moving reliably across multiple source systems.
Which technical decisions matter first?
Consumer needs, source contracts, latency, warehouse or lakehouse fit, quality ownership, lineage and access controls matter first.
Can it connect to our existing systems?
Yes. Operational databases, SaaS APIs, events, files, warehouses, analytics and product services can connect through explicit contracts.
How is the work tested?
We test schemas, completeness, uniqueness, validity, freshness, reconciliation, replay and permission boundaries against consumer expectations.
What do you need before starting?
We need source access, data owners, current jobs, consumer definitions, freshness needs, security constraints and migration priorities.
What affects the delivery timeline?
Source stability, data volume, consumer definitions, platform constraints, migration risk and operating ownership drive delivery.
What boundaries should we agree before delivery?
Automation assists profiling and operations, while schema, quality, access and incident decisions retain accountable owners. Pipelines are idempotent with tested replay. Quality failures have visible impact and owners. Sensitive data is controlled at storage and serving.
Which data needs to become dependable?
Share the source systems, current jobs and the teams or products that use the data.
