Komorebi
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DATA ENGINEERING · ANALYTICS · AI

Data systems that make decisions easier.

Komorebi helps teams turn fragmented data into dependable platforms, governed analytics, useful reporting, and practical AI-enabled workflows.

Talk about your project Explore services
  • Platform-first
  • Business-aligned
  • Security-conscious
From source to decision Designed to scale
01
Ingest

APIs · SaaS · Files · Databases

02
Model

ELT · Lakehouse · Warehouse · dbt

03
Serve

Semantic layer · BI · Data products

04
Activate

Automation · AI · Decision support

ReliableTrusted data foundations
ReusableShared logic and metrics
ObservableClear lineage and quality
UsefulBuilt around real decisions

SERVICES

Build the foundation. Then make it useful.

Engagements can focus on one layer or connect the full path from source systems through analytics, reporting, automation, and AI.

01

Data Engineering

Reliable pipelines and curated data layers designed for maintainability, testing, and scale.

  • ETL / ELT pipelines
  • Lakehouse & warehouse design
  • Batch & incremental processing
  • Data quality & observability
02

Analytics Engineering

Reusable business logic and modeled data that creates one dependable language for the business.

  • Dimensional modeling
  • dbt transformation layers
  • Metric & KPI standardization
  • Semantic model architecture
03

Business Intelligence

Reporting systems designed around decisions—not dashboard volume.

  • Power BI architecture
  • Executive & operational reporting
  • Performance optimization
  • Governance & self-service BI
04

AI & Automation

Practical AI capabilities grounded in governed data and clear operating workflows.

  • RAG-ready data pipelines
  • AI-assisted analytics
  • Workflow automation
  • LLM integration patterns

APPROACH

Architecture before accumulation.

More tools, dashboards, and pipelines do not automatically create a better data environment. Komorebi focuses on the operating model underneath them.

  1. 01

    Understand the decisions

    Start with the business questions, users, workflows, constraints, and trust gaps the system must support.

  2. 02

    Design the data path

    Define source-to-consumption architecture, ownership, transformation boundaries, and reusable business logic.

  3. 03

    Build for operations

    Add testing, version control, documentation, monitoring, and deployment practices so the system can be maintained.

  4. 04

    Enable the next layer

    Once the foundation is dependable, layer on self-service analytics, automation, and AI where they create measurable value.

CAPABILITIES

Modern tools. Deliberate architecture.

Cloud & PlatformsAzure · AWS · Databricks · Snowflake
TransformationSQL · Python · PySpark · dbt
OrchestrationAirflow · Jobs · Scheduled workflows
AnalyticsPower BI · Semantic models · DAX
EngineeringGit · CI/CD · Testing · Documentation
AI EnablementRAG · LLM workflows · Data products

ENGINEERING PRINCIPLE

Build for trust, not just delivery.

Good systems are understandable, testable, governed, and supportable after launch. We prioritize simple architectures, controlled access, reusable components, and clear ownership.

LET'S TALK

Have a data problem worth fixing?

Share what you are trying to build, where the current system is breaking down, and what a successful outcome would look like.

Email Komorebi