Independent expertise. Engineered for scale.
Engineering data
platforms
built to scale.
We design, modernize and optimize cloud data platforms for organizations that depend on reliable, high-performance data infrastructure.
01 / What we do
Complex platforms.
Clear direction.
From the first architecture decision to the systems you run every day. Focused expertise where it matters.
Data Platform Engineering
A reliable foundation for your data. Lakehouse architectures, ingestion frameworks and production-grade pipelines designed for the way your business operates.
Platform Modernization
Move beyond the constraints of legacy systems. A pragmatic path to modern cloud infrastructure, with continuity built into the migration.
Data Platform Assessment
Know where your platform stands. An independent review of architecture, reliability, performance and cost, with a prioritized technical roadmap.
02 / Technical depth
The whole platform.
Not just the pipeline.
We connect architecture, engineering and operations across the modern data stack.
Explore our expertiseIngest
Kafka · Azure Data Factory · APIs
Process & store
Databricks · Spark · Delta Lake · ADLS / S3
Model & serve
Snowflake · dbt · AWS Lambda · APIs
Govern & operate
Unity Catalog · Observability · CI/CD
03 / How we work
A considered approach.
From clarity to delivery.
Engagements shaped around your platform, your team and the work that needs to get done.
Assess
Understand your platform, business requirements, constraints and technical risks.
Architect
Define pragmatic architecture and a clear, sequenced delivery roadmap.
Deliver
Build production-ready solutions alongside your engineering organization.
Scale
Strengthen reliability, governance and performance as your platform grows.
04 / Engagement examples
What the work looks like.
Lakehouse engineering Illustrative example
Databricks Lakehouse: From Source to Analytics
API, SQL Server, ADLS or S3, and Kafka ingestion driven by simple YAML definitions. Databricks bronze tables feed dbt models in silver and gold for BI and analytics.
Warehouse & application integration Illustrative example
Snowflake: Analytics and API Delivery
Configuration-driven ingestion and dbt models in Snowflake, serving BI alongside AWS workers and Lambda functions that push curated data to downstream APIs.
Platform modernization Illustrative example
From Local Files to a Cloud Data Platform
Replace manual file transfers with controlled cloud landing and triggered ingestion: Azure Data Factory to ADLS, or an AWS transfer path to S3, then Databricks or Snowflake.
05 / Why ARGANAUT
Specialist by design.
Accountable by nature.
A founder-led consultancy with a direct line between the people designing your architecture and the people building it.
Meet ARGANAUT- Senior-led delivery
- Deep data-platform specialization
- Architecture and implementation together
- Small, focused engineering teams
- Direct communication
- A lean consulting model
A clearer way forward
Your data platform should accelerate the business,
not constrain it.
Let’s discuss what you’re building.