Cloud Data Warehouse Design
Snowflake, BigQuery, Redshift, or Databricks — chosen and architected for your workload. Multi-environment, role-based access, cost-optimised from day one.
Data Engineering & ETL
Data engineering & ETL pipelines built for production reliability
Resilient batch and real-time pipelines on Airflow, dbt, Snowflake, Databricks, and BigQuery — with observability, data quality, and lineage built in from day one. The foundation analytics, BI, and ML actually run on.
Why pipelines break trust
Data warehouses don't fail loudly. They fail quietly — a column type changes, a source API rate-limits, a backfill misses a partition, a join doubles a row count. By the time the dashboard looks suspicious, leadership has already made decisions on bad numbers.
The result: analysts stop trusting the warehouse, engineers stop trusting the analysts, and the organisation reverts to spreadsheet-driven decisions. The warehouse becomes a cost centre instead of a competitive advantage.
Trustworthy by construction
We build pipelines the way we build software: version-controlled, tested, observable, and reviewable. Every model is paired with schema tests and freshness checks. Every transformation is documented with lineage. Every load runs idempotently — re-running a job never produces a different result.
The standard we hold ourselves to: an engineer joining your team six months from now should be able to read the pipeline and understand exactly what it does, why, and how to safely change it.
What We Deliver
What we deliver
From greenfield warehouse design to incremental modernisation of legacy pipelines — chosen to match your data volume, team maturity, and cost profile.
Batch ETL/ELT Pipelines
Production-grade pipelines on Airflow, Dagster, Prefect, or Mage, with dbt for in-warehouse transformations. Idempotent, backfillable, and CI-tested.
Real-time & Streaming Pipelines
Kafka, Kinesis, Pub/Sub, or Debezium-based change data capture. With exactly-once semantics where it matters and at-least-once where it's good enough.
Data Quality & Observability
Schema tests, freshness checks, anomaly detection, and lineage tracking with dbt tests, Great Expectations, Soda, or Monte Carlo. Alerting that actually points to root cause.
Data Lake & Lakehouse
Delta Lake, Iceberg, or Hudi-based lakehouse architectures on S3, ADLS, or GCS. For unstructured, semi-structured, and ML-ready data at scale.
Migration & Modernisation
Move off legacy on-prem warehouses, retire SSIS/Informatica jobs, or consolidate fragmented pipelines onto a modern stack — domain by domain, with no big-bang risk.
How We Work
A delivery process built for measurable outcomes
From scoping to handover, every engagement follows the same disciplined cadence — designed to remove ambiguity and ship results.
Architecture & Diagnostic
We assess current pipelines, source systems, and consumption patterns. Output: a target architecture and a sequenced migration plan with cost projections.
Foundation
Standing up the warehouse, orchestrator, transformation framework (dbt), and the testing/observability layer. Done before the first business pipeline ships.
Domain-by-Domain Build
Each business domain (sales, finance, product, marketing) is delivered as an independent slice — model, test, deploy, validate with stakeholders, move on.
Operate & Hand Over
Runbooks, on-call playbooks, and pairing sessions until your team owns the system. We measure success by how little you need us after handover.
Tools & Stack
Production-grade stack, vendor-neutral choices
We pick the right tool for the job — and document why — so you're never locked into a stack that doesn't fit your team.
Snowflake
Databricks
BigQuery
Redshift
Apache Airflow
Dagster
Prefect
dbt
Apache Kafka
AWS Kinesis
Debezium (CDC)
Delta Lake / Iceberg
Fivetran / Airbyte
Great Expectations
Monte Carlo
Terraform
Outcomes
Outcomes you can expect
Indicative ranges based on typical client engagements. Every project ties to a measurable KPI agreed at kickoff.
99.9%
pipeline freshness SLA after observability rollout
50–80%
reduction in pipeline incidents and data quality fires
30–60%
cloud warehouse cost reduction through query and model tuning
2–3x
faster time-to-ship for new analytics use cases
FAQ
Frequently asked questions
Should we use Snowflake, BigQuery, or Databricks?
It depends on your workload mix and ecosystem. Snowflake is strongest for governed analytics and Marketplace data sharing. BigQuery wins for serverless economics and tight integration with the Google ecosystem. Databricks is the right choice when you need first-class ML and lakehouse semantics. We recommend based on your data volume, team skills, and 3-year cost profile — not vendor partnerships.
Do you build batch pipelines, real-time pipelines, or both?
Both. Most engagements use a hybrid: batch for cost-efficient analytical workloads and CDC or streaming (Kafka, Kinesis, Pub/Sub, Debezium) where the business actually needs sub-minute freshness. Real-time isn't free — we recommend it only where the business case justifies the cost.
Can you work with our existing data stack or do you require a rebuild?
We work with your existing stack. Most engagements involve incremental modernisation — adding dbt to a legacy SQL pipeline, instrumenting Airflow with proper observability, or migrating one domain at a time to a cloud warehouse. We avoid big-bang rewrites unless the existing system is genuinely beyond repair.
How do you handle data quality and pipeline failures?
Every pipeline ships with schema tests, freshness checks, anomaly detection, and structured alerting (PagerDuty, Slack, or email). We treat data quality as a first-class deliverable — not an afterthought. Tools we use include dbt tests, Great Expectations, Soda, and custom validators.
Do you offer ongoing pipeline support after delivery?
Yes — many clients retain us on a part-time embedded basis for ongoing platform evolution, on-call backup, and quarterly cost/performance reviews.
Related Services
Explore complementary capabilities
Pipelines you can trust before the next board meeting
Book a 30-minute consultation. Walk us through your current data stack — sources, warehouse, orchestrator, dashboards — and we'll come back with the highest-ROI improvement to ship first.
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