ETL & ELT pipelines
Orchestrated, version-controlled pipelines in Airflow and dbt that are testable and easy to reason about.
Services
Data Engineering
Intelligent data infrastructure that scales.
Apache SparkAirflowdbtKafka
Intelligent data infrastructure that scales.
Great analytics and AI start with data infrastructure you can trust. We design and build the pipelines, warehouses and streaming systems that move your data reliably from where it is created to where decisions get made.
Our team works fluently across batch and real-time: orchestrated ETL with Airflow and dbt, large-scale processing with Apache Spark, and event streaming with Kafka. We build for correctness first — idempotent pipelines, data quality checks, and observability so you know the moment something drifts.
The result is a single source of truth: clean, documented, analytics-ready data that powers dashboards, machine learning models, and the day-to-day reporting your team actually relies on.
What we deliver
The core capabilities inside our data engineering engagements.
Real-time streaming
Event-driven architectures with Kafka for use cases that cannot wait for a nightly batch.
Big data processing
Apache Spark jobs that process large volumes efficiently and cost-consciously.
Data warehousing
Well-modelled warehouses (star/snowflake schemas) that make analytics fast and cheap to query.
Data quality & observability
Automated checks, freshness monitoring and alerting so bad data never reaches a dashboard silently.
Migration & consolidation
Move off fragile scripts and scattered sources into one governed, documented platform.
Who it's for
Is this the right fit?
We do our best work when the problem is a genuine fit. Data Engineering typically makes sense for:
Companies whose reporting breaks or lags as data volume grows
Teams that need real-time data for operations, not just next-day batches
Businesses consolidating data scattered across many tools into one warehouse
Organisations preparing clean, reliable data to feed machine learning
FAQ
Common questions
What tools do you use for data pipelines?
We primarily use Apache Airflow and dbt for orchestration and transformation, Apache Spark for large-scale processing, and Kafka for real-time streaming. The exact stack depends on your data volume and latency needs.
Can you fix our existing broken pipelines?
Yes. A large share of our data work is stabilising pipelines that have grown brittle — adding tests, observability and proper orchestration so they stop failing silently.
Do you build both batch and real-time systems?
Both. We design batch pipelines for reporting and analytics, and real-time streaming architectures where operations depend on up-to-the-second data.
Let's build
Ready to start?
Tell us what you're trying to build. We'll tell you honestly whether we're the right team — and how we'd approach it.
Talk to us