Service Capability
Data & Analytics
Data platform engineering, pipeline development, business intelligence, and analytics infrastructure for organisations that need reliable, governed, and scalable data capabilities.
What we do
Most data problems are not analysis problems. They are engineering problems: data that doesn't arrive reliably, pipelines that break silently, schemas that drift, and reporting that produces different numbers depending on which tool you use. We fix the engineering layer first.
Our data practice covers data platform architecture (warehouse, lakehouse, and streaming), ELT/ETL pipeline development, real-time stream processing, BI and dashboarding, and data governance frameworks that maintain quality and lineage across the entire data estate.
We understand both the producer and consumer sides of data. We work with engineering teams who create data as a by-product of operations, and with analytics teams who need that data to be reliable, documented, and queryable. Designing for both is what makes a data platform sustainable rather than just functional in year one.
Problems we address
- — Data is fragmented across systems, spreadsheets, and departmental databases with no single source of truth
- — Reporting is manual, slow, and produces inconsistent results across teams using the same underlying data
- — Existing pipelines are brittle, undocumented, and break when source systems change
- — Analytics queries are slow because the data model was designed for transactional systems, not analytical workloads
- — Data quality is unknown — no validation, no monitoring, and no alerts when records are missing or malformed
- — Regulatory and audit requirements for data lineage, access control, and retention are not currently met
Capabilities
Data Platform Architecture
Warehouse, lakehouse, and hybrid architecture design using Snowflake, Databricks, BigQuery, or Redshift. We design for query performance, cost, and the data volume and velocity your workloads actually require.
ELT/ETL Pipeline Development
Reliable, tested, and observable data pipelines built with dbt for transformation and Apache Airflow for orchestration. Pipelines include data quality checks, alerting, and clear lineage documentation.
Real-Time Streaming
Event streaming architectures using Apache Kafka and Apache Spark Structured Streaming for near-real-time analytics, CDC (change data capture) from operational databases, and event-driven data delivery.
BI & Dashboarding
Semantic layer design and dashboard development in Tableau or Power BI. We build self-service analytics environments that give business users access to trusted data without requiring SQL knowledge or engineering intervention.
Data Governance & Quality
Data quality rules, profiling, and automated validation integrated into pipelines. Data cataloguing, ownership assignment, and retention policy implementation to satisfy audit, compliance, and GDPR requirements.
Metadata & Lineage Management
End-to-end data lineage tracking so analysts and auditors can trace any metric back to its source system. We implement OpenLineage-compatible tooling that integrates with your existing data catalogue or data mesh implementation.
Our approach
Understand producers and consumers first
Before designing any model or pipeline, we map the systems that produce data and the questions that consumers are trying to answer. Data platforms that optimise for the storage layer without understanding the consumption layer almost always need to be redesigned within two years.
Data contracts as a forcing function
We establish data contracts between producers and consumers that define schema, quality expectations, and SLAs. Contracts create accountability and make schema changes a deliberate, coordinated act rather than a surprise that silently breaks downstream pipelines.
Incremental build, production from the start
We don't build a complete data model in a sandbox and then migrate. We build incrementally in production, starting with the highest-value data domains, so business users are getting value from week four — not month eight.
Observability built in
Every pipeline we deliver includes monitoring, alerting, and data quality assertions. Pipeline failures are caught before they affect downstream consumers, not discovered by analysts noticing discrepancies in reports.
Delivery process
-
1
Discovery & data landscape mapping
Identify all data sources, consumers, existing pipelines, and analytical use cases. Assess current quality, latency requirements, and regulatory constraints.
-
2
Data model & platform design
Logical and physical data model for warehouse or lakehouse layer. Technology selection, access control design, and cost modelling for expected query volumes.
-
3
Pipeline build & ingestion
Build ingestion pipelines for prioritised data domains, including quality checks, transformations, and scheduling. Deployed to production incrementally by domain.
-
4
BI layer & self-service enablement
Semantic models and dashboards built against the warehouse. Self-service training and documentation for business analysts to create reports independently.
-
5
Monitoring & ongoing governance
Pipeline health dashboards, data quality metric tracking, cost monitoring, and governance process handover to your data team.
Technologies
Data Warehouses & Lakehouses
Processing & Orchestration
BI & Visualisation
Databases
Governance & Cataloguing
Languages
Industries served
Frequently asked questions
Turn your data into a reliable asset
Whether you need to modernise a legacy data warehouse, build streaming pipelines, or establish data governance, our data engineering team can help.