Data Architecture
& Engineering
Designing and building scalable, resilient data platforms that power analytics, AI and real-time insight across your enterprise — reliably and cost-effectively.
The Foundation of a Data-Driven Enterprise
Every successful analytics and AI capability is built on well-engineered data infrastructure. Our practice designs and builds scalable, resilient platforms that allow organisations to ingest, process, store and serve data at enterprise scale — reliably, securely and cost-effectively.
Modern Data Platform Design
We design data architectures suited to your scale and workload profile — whether a cloud data warehouse, a lakehouse, a data mesh with domain ownership, or a hybrid architecture combining multiple patterns for different analytical and operational needs.
Data Engineering at Scale
Our engineering team builds robust ELT/ETL pipelines, data products and streaming architectures on Databricks, Snowflake, AWS Glue, Azure Data Factory, dbt and SAP Datasphere — ensuring data is always pipeline-ready and analytics-grade.
What We Deliver
Data Platform Design
Lakehouse, warehouse and data mesh architecture design — validated for scalability, cost and security before build.
Pipeline Engineering
Batch and streaming pipeline development using Spark, Kafka, dbt and cloud-native tooling — with quality checks built in.
DataOps & CI/CD
DataOps practices and CI/CD for data platform releases — automated testing, lineage and observability at every stage.
Data Quality
Data quality framework implementation using Great Expectations, dbt tests and custom rule engines — trust your data.
Our Core Services
Architecture Design & Validation
We design and review data platform architectures — validating scalability, cost, security and performance characteristics before a single resource is provisioned.
- Data architecture patterns assessment (warehouse, lakehouse, mesh)
- Cloud-native technology selection and stack design
- Cost modelling and infrastructure right-sizing
Data Pipeline Engineering
Building reliable, observable, maintainable data pipelines — with data quality checks, lineage tracking and automated alerting built in from day one.
- Batch and streaming pipeline development (Spark, Kafka, Flink)
- Data quality framework implementation (Great Expectations, dbt tests)
- DataOps and CI/CD pipeline for data platform releases
SAP Data Platform Engineering
Building enterprise data platforms in SAP Datasphere, SAP BTP and SAP HANA — integrated with cloud data lakes and third-party warehousing platforms.
- SAP Datasphere space and data product design
- SAP HANA modelling and performance optimisation
- SAP BTP integration with Azure Data Lake and Snowflake
Data Observability & Lineage
Implementing end-to-end data observability — monitoring freshness, completeness, schema changes and anomalies across your entire data estate in real time.
- Data lineage tracking across pipelines and reports
- Anomaly detection and freshness alerting
- Data incident management workflow and escalation
Build Your Data Foundation
Engage our data engineering team to design and build the data platform your analytics and AI ambitions deserve.