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Services

Data architecture first. Everything else follows.

Six service areas covering the full life of a data platform, from the first architecture sketch to models, pipelines, cloud platforms and the AI use cases built on top.

Data Architecture & Strategy

A data platform designed on purpose, not one that grew by accident.

We design the target architecture for your data: how sources are integrated, where data is stored and modeled, how it is served to people and systems, and how it evolves. Every decision is written down with its trade-offs, so your team understands not only what to build but why.

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Challenges we solve

  • No one can draw the whole data landscape on one page
  • Warehouse, lake and SaaS tools overlap and compete
  • A modernization or cloud migration with no clear target
  • Technical decisions made project by project, never as a whole

Typical deliverables

  • Current-state assessment and architecture diagnosis
  • Target architecture: warehouse, lakehouse or hybrid
  • Integration architecture and data flow design
  • Architecture decision records and standards
  • Prioritized roadmap with effort and risk
  • Architecture walkthroughs with your team

Capabilities

  • Enterprise data architecture
  • Data warehouse
  • Lakehouse
  • Medallion architecture
  • Data integration
  • Architecture reviews
  • Technology assessment

Data Modeling & Governance

One customer, one order, one revenue, across the whole company.

This is where most data problems are really solved. We model how your business works, from core entities to the metrics leadership reports, using dimensional modeling, Data Vault and enterprise models where each fits best. Then we put the governance around it so it stays right: definitions, ownership, quality rules and lineage.

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Challenges we solve

  • Every department has its own version of the truth
  • Historical reports change when master data changes
  • Integrating many sources without endless rework
  • Nobody knows where a number comes from or who owns it

Typical deliverables

  • Conceptual, logical and physical data models
  • Dimensional models: star schemas, conformed dimensions, bus matrix
  • Data Vault integration layer: hubs, links, satellites
  • Business glossary and metric definitions
  • Data quality rules, metadata and lineage design
  • Master data management (MDM)
  • Modeling workshops for your analysts and engineers

Capabilities

  • Dimensional modeling (Kimball)
  • Data Vault
  • Slowly changing dimensions
  • Semantic layer
  • MDM
  • Data quality
  • Metadata & lineage

Data Engineering

Pipelines that run quietly every day, and tell you when they don't.

We build the pipelines that move data from your systems into the model: ingestion from databases, APIs and files, batch and streaming processing, and orchestration that recovers from failure. dbt is our specialty for the transformation layer: modular models, tests, documentation and lineage, all versioned and reviewed like any other code.

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Challenges we solve

  • Fragile scripts and jobs that fail silently
  • Loads that take hours and miss their window
  • Manual fixes before every month-end close
  • Data arriving too late to be useful

Typical deliverables

  • ETL and ELT pipelines for databases, APIs and files
  • Batch, incremental, CDC and streaming ingestion
  • dbt projects: models, tests, docs and CI
  • Orchestration, alerting and recovery
  • Data quality tests and pipeline observability
  • Pairing sessions and runbooks for handover

Capabilities

  • SQL
  • Python
  • dbt
  • Apache Airflow
  • Apache Kafka
  • Airbyte
  • Spark
  • CDC

Cloud Data Platforms

The right platform for your data, tuned for performance and cost.

We design, implement, migrate and optimize cloud and hybrid data platforms on AWS, Azure and Google Cloud. We have delivered on Snowflake, Databricks, BigQuery, Redshift and Dremio, on lakehouses with Apache Iceberg, and on fully open-source stacks, including our own tuned PostgreSQL with columnar storage. We are cloud-agnostic with a clear favorite: Snowflake, where we hold the SnowPro Advanced: Architect certification. Still, the recommendation follows your data, your team and your budget.

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Challenges we solve

  • Cloud bills growing faster than the value delivered
  • Slow queries and processing windows that keep slipping
  • Migrating from on-premises or legacy platforms
  • Vendor lock-in and licensing costs

Typical deliverables

  • Platform selection and reference architecture
  • Migration plan and execution
  • Platform training for your team
  • Performance diagnosis and tuning
  • Cost optimization (FinOps for data)
  • Security, access control and environment governance
  • Open-source data stack design

Capabilities

  • Snowflake
  • Databricks
  • BigQuery
  • Amazon Redshift
  • Dremio
  • Apache Iceberg
  • Columnar PostgreSQL
  • AWS
  • Azure
  • Google Cloud

Technical Advisory & Mentoring

A senior architect in your corner for the decisions that matter.

Sometimes you don't need a project, you need a trusted second opinion. We review architectures and designs, support high-impact technical decisions, and mentor data teams to raise the bar on how they model, build and operate. Training is part of our DNA: our founder teaches graduate courses and has delivered more than 60 trainings and classes.

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Challenges we solve

  • A big technical decision and no one to challenge it
  • A capable team that needs senior guidance
  • Inconsistent practices across projects
  • Modernizing the stack without stopping delivery

Typical deliverables

  • Architecture and design reviews
  • Technical due diligence and assessments
  • Fractional data architect or technical lead
  • Team mentoring and code reviews
  • Custom trainings: modeling, dbt, Dremio and more
  • Engineering standards and best practices

Capabilities

  • Architecture reviews
  • Technical leadership
  • Mentoring
  • Training
  • Best practices

AI & Data IntegrationExpanding

AI is only as good as the data model underneath it.

AI can't fix chaos: it needs structure. Point an assistant at raw tables and it will answer confidently and wrongly. We prepare your data for AI the way we prepare it for people: modeled, documented and governed. Then we design how AI applications, retrieval systems and agents access it, with the right context and the right permissions.

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Challenges we solve

  • AI pilots that give plausible but wrong answers
  • No shared definitions for AI to rely on
  • Sensitive data exposed to AI tools without control
  • Unstructured content disconnected from business data

Typical deliverables

  • AI-readiness assessment of your data foundation
  • Semantic layer and metric definitions for AI use
  • Retrieval (RAG) and semantic data architecture
  • Warehouse integration with AI services
  • Governed data access for AI agents

Capabilities

  • Semantic layer
  • RAG
  • Vector search
  • MCP
  • Governed access
  • AI-assisted analytics
Get started

Not sure where to start?

Most engagements start with an assessment. In a few weeks you get a clear diagnosis, a target architecture and a prioritized roadmap, and you decide what comes next.

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