01
Data warehouse architecture on Snowflake
Warehouse design, schema architecture, performance tuning, cost optimisation. As a Snowflake partner, we ship warehouse engagements end-to-end — from greenfield builds to migrations from legacy platforms.
Capabilities · Technology · Data Lake & Warehouse
Data foundations built to run on.
A data engineering practice that ships warehouse architecture, modeling, governance, and the semantic layer underneath modern analytics and AI work. Built across Snowflake, Databricks, and dbt — the platforms most modern data stacks now run on.
02 · What we build
The data foundation work we ship most often.
Seven capabilities that show up across most engagements. Some are featured by partnership depth, some by methodology, some because they're the layer most modern data work runs on.
02
Data modeling and transformation with dbt
Analytics engineering with dbt at the centre. As a dbt partner, we build dimensional models, transformation layers, testing frameworks, and documentation that the client's team owns and extends after handoff.
03
Lakehouse architecture
Where the work calls for it — unified data lake plus warehouse patterns on Databricks or Snowflake. Most of our engagements lead with warehouse architecture; lakehouse fits when the data volume, latency, or workload mix justifies it.
04
Streaming and real-time data pipelines
Production streaming pipelines for use cases where batch isn't enough — real-time event ingestion, change-data-capture, and low-latency analytics surfaces. The streaming platform is chosen per engagement based on client environment.
05
Data governance, lineage, and quality
Governance frameworks built around data contracts, lineage, access control, and quality testing. We work in the catalog and governance platforms our clients have selected — the discipline is the work, not the specific tool.
06
Semantic and metric layers
A single source of truth for business metrics — built primarily with the dbt Semantic Layer, and adapted to other semantic platforms where the client's stack requires it. The metric definitions stay consistent across BI tools, AI workflows, and downstream consumers.
07
Data architecture and platform migration
Foundational data architecture decisions — platform selection, migration planning, modernisation roadmaps. The work that decides where the data lives, who owns it, and how it gets to the systems that consume it.
03 · How we think about it
Three things we believe about data foundations.
Belief 01
The platform decision is a long-term decision.
The choice of warehouse, lakehouse, or hybrid pattern shapes everything downstream — performance, cost, governance, team workflow, AI readiness. We treat platform selection as architecture, not procurement. The right answer depends on the workload mix, the team, and where the data needs to go in five years.
Belief 02
The model is where governance actually lives.
Governance frameworks fail when they're bolted on after the data is already moving. We model for governance from the start — data contracts, lineage, ownership, and quality testing built into the transformation layer with dbt rather than enforced through a separate catalog after the fact. The catalog tools matter; the discipline matters more.
Belief 03
The semantic layer is where business logic should live.
Business metrics defined in BI dashboards drift. Metrics defined in spreadsheets break. The semantic layer is where business definitions belong — versioned, governed, and consumed consistently by every BI tool, AI workflow, and analytical surface downstream. It's the layer most data stacks underinvest in until something breaks.
04 · Tools and ecosystem
The platforms and tools we work in.
The data platforms, transformation tools, and governance products we ship across — named honestly, with the strategic partnerships called out separately.
Data Platforms
SnowflakeDatabricksBigQueryRedshiftMicrosoft Fabric
Modern cloud data platforms where most warehouse and lakehouse engagements live.
Transformation & Modeling
dbtSQLPythonAirflowSnowpark
Analytics engineering with dbt at the centre, orchestrated and extended where the work needs it.
Governance & Semantic
dbt Semantic LayerCatalog platformsLineage toolsQuality frameworksRBAC patterns
Governance and semantic-layer work aligned to client's catalog choice.
Strategic Partnerships
Snowflakedbt
05 · FAQ
Questions a Head of Data usually asks first.
Four things we get asked early in data platform conversations. The honest answers, so you can decide whether a working session is worth your time.
01Snowflake or Databricks — how do you help us decide?+
Both are strong platforms; the right one depends on your workload mix. Snowflake tends to fit when SQL-first analytics, ease of operations, and governance simplicity are priorities. Databricks tends to fit when ML workloads, lakehouse patterns, and unified data engineering with data science are dominant. We work across both as partners or in client environments — and we'll walk through the decision in the first working session.
02Do you work in our existing data stack, or do you push a migration?+
We work in what you have when it makes sense. Most engagements run in the client's existing Snowflake, Databricks, or hybrid platform. If a migration is genuinely the better answer for the work ahead, we'll say so honestly and scope it accordingly — but a migration isn't the default recommendation.
03How do you approach governance without slowing the data work down?+
We build governance into the transformation layer rather than bolting it on after. Data contracts, lineage, ownership, and quality testing live in dbt and the modeling discipline itself — so governance becomes part of how the team builds, not a separate gate. The catalog and platform-specific governance tools we then integrate are chosen to fit the team you have, not imposed.
04What happens to the work when the engagement ends?+
Your team owns it. The warehouse, the dbt project, the documentation, the semantic-layer definitions — all of it transfers fully. No SaaS layer between you and your data. No licensing on top of the data platform you already pay for. The intelligence is yours from handoff.
Most data work breaks at the modeling layer. The platform is rarely the problem.
07 · Ready when you are
Tell us where your data foundation is breaking. We'll tell you what we'd build.
A working session with a senior data engineer — 30-45 minutes, focused on your current platform, your modeling pain, your governance posture. No commitment. We leave you with useful thinking either way.
No commitment30–45 minutesSenior data engineer on the call
Related technology capabilities
The rest of the technical surface we work across.
Technology
Applied AI
LLM integration, SLMs, fine-tuning open-source, predictive ML, and the engineering layer that makes models reliable in production.
TechnologyComputer Vision
Vision systems for operational intelligence — what's happening across stores, sites, and physical environments, on a continuous loop.
TechnologyNewAgentic AI
AI agents that reason and act inside your systems — not just answer questions. Built on top of the data foundation.
TechnologyCloud Infrastructure
Deployment and operations across GCP, AWS, and Azure — including on-customer-infrastructure and sovereign cloud targets.
Founded 2020 · AI & ML engagements delivered across North America, Australia, and India · Partnerships and methodology details on About