Your data,
actually reliable.
Finally.
We build the pipelines, warehouses, and data models that your business runs on — fast, tested, observable, and owned by your team — not locked in someone's laptop.
The architecture
Every layer of your
data stack, covered.
Ingest
Ingest
Pull data from any source — structured or unstructured, batch or real-time.
- REST APIs
- Webhooks
- CDC Streams
- File Uploads
- Event Queues
Transform
Transform
Clean, enrich, join, and reshape data into reliable, tested models.
- dbt Models
- Spark Jobs
- Python ETL
- SQL Pipelines
- Data Quality
Store
Store
The right storage layer for the right query pattern — cost-optimised.
- Data Warehouse
- Data Lake
- Feature Store
- Time-series DB
- Graph DB
Serve
Serve
Reliable data delivery to every downstream consumer — humans and systems.
- Analytics APIs
- BI Dashboards
- ML Pipelines
- Real-time Feeds
- Exports
Sound familiar?
Problems we fix
every single week.
"Your dashboards lag behind reality"
We rebuild pipelines for freshness — sub-hour latency is the baseline, sub-minute where it matters.
"Nobody trusts the numbers"
Data quality tests, lineage, and anomaly detection so every metric has a provenance trail.
"Pipelines break silently overnight"
Observability, alerting, and automated recovery built into every pipeline we deliver.
"Your data team is drowning in ad hoc requests"
A well-modelled semantic layer means analysts self-serve instead of waiting on engineering.
"Cloud bills are out of control"
We audit query patterns, partitioning, clustering, and materialisation strategies to cut costs — typically 40–60%.
What we build
End-to-end data
engineering services.
Data Pipeline Engineering
Robust ELT/ETL pipelines that handle schema drift, late-arriving data, and partial failures gracefully. Built with full observability from day one.
Data Warehouse & Lakehouse
Design and implement modern analytics architectures on Snowflake, BigQuery, or Databricks — structured for performance and cost efficiency at any scale.
Real-time Streaming
Event-driven architectures that process millions of events per second. Kafka, Flink, and Kinesis pipelines built for sub-second latency and zero data loss.
Data Quality & Observability
Automated data quality tests, freshness monitoring, lineage tracking, and anomaly alerting so data issues are caught before they reach dashboards.
ML Data Infrastructure
Feature stores, training data pipelines, experiment tracking, and model serving infrastructure — the data layer that makes ML systems production-ready.
Data Governance & Catalog
Metadata management, access controls, PII classification, and data cataloguing so your entire organisation can find and trust what they need.
How we work
From messy sources
to trusted datasets.
Data Audit & Architecture
We map your existing data sources, understand downstream use cases, and design a target architecture that fits your scale, team, and budget — not just a reference pattern.
Foundation & Core Pipelines
We establish the foundational layer: warehouse setup, ingestion framework, orchestration platform, and your first 3–5 production pipelines with full CI/CD.
Modelling & Transformation
Business logic encoded as tested, documented dbt models. Semantic layer definitions. Everything in version control, reviewed like application code.
Quality & Observability
Data quality tests at every layer, freshness SLAs, lineage graphs, and alerting pipelines so your team knows about data issues before your stakeholders do.
Handover & Enablement
Complete documentation, runbooks, and live training sessions for your team. We don't disappear at launch — we make sure your team can own it fully.
Our stack
The modern data stack,
properly implemented.
Orchestration
- Apache Airflow
- Prefect
- Dagster
- dbt Cloud
- GitHub Actions
Warehouses
- Snowflake
- BigQuery
- Redshift
- Databricks
- ClickHouse
Streaming
- Apache Kafka
- Apache Flink
- Kinesis
- Pub/Sub
- Redpanda
Observability
- Monte Carlo
- Great Expectations
- DataHub
- OpenLineage
- Grafana
Common questions
FAQ
We already have a data warehouse — can you work with it?
Yes. We work with whatever warehouse you have. We're not attached to any vendor — we pick the right tools for your situation and can migrate or optimise existing infrastructure.
How do you handle sensitive / PII data?
PII classification, tokenisation, masking, and role-based access controls are built into the architecture design phase. We also help with GDPR deletion pipelines and audit log requirements.
What's the difference between data engineering and analytics engineering?
Data engineering covers the infrastructure and pipelines that move and store data reliably. Analytics engineering (our dbt work) sits on top — modelling raw data into clean, trusted business metrics. We do both.
Do you provide ongoing support after delivery?
Yes. We offer retainer agreements for pipeline maintenance, schema change handling, new source integrations, and on-call support. Most clients keep us on a light retainer after the initial build.
Can you help us migrate from an on-prem data warehouse to the cloud?
Absolutely — this is one of our most common engagements. We handle schema migration, historical data backfill, cutover planning, and parallel-run validation to ensure zero data loss.
Stop explaining
why the numbers differ.
Let us audit your current data stack — free, no commitment. You'll walk away with a clear picture of what's broken, what's fixable, and what it'll take to fix it.