Data Engineering & Analytics
Enterprise data platforms, cloud data warehouses, and analytics layers that power decision-making at scale.
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What This Engagement Covers
Data platforms fail when they're designed for the first use case and not the tenth. We build enterprise data platforms that are designed to scale — from ingestion through transformation to consumption — on the cloud infrastructure that matches your regulatory and cost requirements.
Included in scope
- Cloud data warehouse design and implementation (Snowflake, Databricks, BigQuery, Redshift)
- ELT pipeline engineering at enterprise scale
- dbt transformation layer and semantic data modelling
- Real-time streaming architecture (Kafka, Kinesis)
- Enterprise BI and analytics (Power BI, Tableau, Looker)
- Data mesh and data product architecture
When to Engage
Situations We're Built For
"We're migrating off an on-premise data warehouse."
Cloud migrations fail without proper data modelling and governance. We design the target architecture before moving a single byte.
"Our data team spends 80% of time on pipeline maintenance."
We rebuild pipelines with proper monitoring, alerting, and documentation so your data engineers can spend time on analysis, not firefighting.
"Business users can't self-serve data."
We build a semantic layer and BI environment so the business can answer their own questions without a data team ticket.
How We Work
Our Approach
Architecture design
Source systems, ingestion patterns, storage, transformation, and consumption layers. Designed for your scale and regulatory requirements.
Foundation build
Core infrastructure, security, and the first data products before expanding.
Pipeline engineering
Reliable, monitored, tested pipelines with SLAs. Not notebooks.
Semantic layer
dbt models and BI-ready views that make data accessible to the business.
Observability
Data quality monitoring, lineage, and alerting so you know when something breaks before the business does.
Technologies
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