Data Engineering
Reliable data pipelines at the scale your business demands.
We build the data infrastructure that powers your analytics, reporting, and AI — high-throughput ingestion pipelines, cloud data warehouses, and streaming architectures that handle billions of events without breaking.
What we deliver
Every engagement is scoped, priced, and delivered against these capabilities.
Data Pipeline Development
Batch and streaming pipelines with Apache Spark, Flink, and dbt. Ingestion from databases, SaaS APIs, event streams, and file stores.
Cloud Data Warehouse
Snowflake, BigQuery, and Redshift — schema design, partitioning strategy, query optimisation, and cost management.
Lakehouse Architecture
Delta Lake and Apache Iceberg on S3 or Azure Data Lake. ACID transactions, time-travel queries, and schema evolution.
Real-Time Streaming
Apache Kafka, AWS Kinesis, and Flink for sub-second event processing. Exactly-once delivery and stateful stream processing.
Analytics Engineering
dbt models, data marts, and semantic layers. Self-service analytics for your business teams with clean, documented data models.
Data Observability
Monte Carlo, Great Expectations, and custom monitoring for data freshness, schema drift, and quality anomalies — before they hit dashboards.
How we work
A predictable process that keeps you informed and in control at every stage.
Data landscape audit
Map your sources, consumers, current tools, and the pain points costing you most.
Architecture design
Choose the right stack for your volume, latency requirements, and team skills.
Build & validate
Iterative pipeline builds with data quality tests and stakeholder sign-off at each stage.
Productionise & monitor
SLA-backed pipelines, alerting, and runbooks your team can operate confidently.
Technologies we use
Processing
Warehouses
Streaming
Orchestration
Is your data stack slowing your team down?
We'll identify the bottlenecks and propose a fix in the first week.
Response within 24 hours. NDA available on request.