Skip to content

Profile

Who I am

Charnrit Khongthanarat - Senior BI Engineer / BI Consultant based in Bangkok, Thailand.

Charnrit Khongthanarat, senior BI engineer and BI consultant

I'm Charnrit Khongthanarat, a Senior BI Engineer / BI Consultant based in Bangkok, Thailand. Over the past 8+ years I've worked across data and analytics for banking, retail, HR, and enterprise teams — including 6+ years in BI engineering — long enough to see “just one more measure” bring down a production report. What I care about now is the opposite: semantic models under source control, DAX changes with an automated risk check, and deployments a colleague can review before they ship.

I build BI the way good teams build software — source control, review, validation, measured performance, honest rollback paths. Inside Power BI that means PBIP, TMDL, and PBIR; around it, SQL and Databricks where the models are fed, and Python where the workflow around the model lives. Recent applied-AI work extends that same pattern into agent harnesses, RAG, fallback, eval, and human approval for operational data problems.

The testimonial library repeatedly points to technical depth, clear communication, and delivery reliability. That is the working style I try to keep: useful under pressure, without adding drama.

Methodology

How I ship BI

BI as production code

The same five steps, repeatedly.

Power BI engineering should flow through the same gates as application code: source control, review, validation, deploy, monitor. The methodology I ship against is deliberately ordinary; the discipline is in never skipping a step.

  1. Step 1

    Source

    PBIP, TMDL, and PBIR under Git. The model is plain text, not a binary file.

  2. Step 2

    Review

    Every change is a diff a reviewer can read line by line before it goes anywhere.

  3. Step 3

    Validate

    Pattern-based DAX risk checks and AI-assisted test drafting, with humans approving.

  4. Step 4

    Deploy

    Review-gated promotion. Nothing ships without an explicit approval step.

  5. Step 5

    Monitor

    Performance, freshness, and quality tracked as numbers, not adjectives.

Availability

Best-fit roles

  • Senior BI Engineer embedded on a product, data, or analytics team
  • Analytics Engineer or Semantic Model Engineer focused on reporting architecture and performance
  • BI Consultant for reporting migration, governance visibility, and model quality
  • Applied AI builder for review-gated agent workflows around operational data problems
  • Speaker for practical Power BI, PBIP, and AI-assisted BI workflow topics

Currently open to consulting engagements, embedded BI roles, and speaking invitations.

Skills

What I'm strongest at

BI & Reporting

Power BI DAX Paginated Reports Semantic Modeling PBIP TMDL PBIR Tabular Editor

Data & Platform

SQL Python PySpark Azure Databricks Azure Data Lake Azure OpenAI Delta Lake ETL / ELT Git

Quality & Governance

Dashboard Performance Tuning Data Quality Scorecards Validation Workflow Design

Consulting & Communication

Stakeholder Management Solution Shaping Thai / English Communication Community Speaking

Approach

How I work

BI as production code

Source control, review, validation, rollback. The model is code; the code is reviewed; the deployment is traceable. Power BI included, not excluded.

Measured, not claimed

Performance, change risk, and data quality should be numbers, not adjectives. Before/after on the dashboard, risk checks on the DAX, a scorecard on the data.

AI under human review

AI helps me write measure-validation logic, surface DAX risk, draft docs, and prototype agent workflows. Production changes and high-risk actions still ship through a person and a review step. Useful, not unchecked.

What I won't ship

Semantic models I can't re-deploy under review. Dashboards I can't explain the freshness of. AI outputs I haven't checked.

Career

Career summary

Accenture Thailand

Oct 2022 - Present

Vis & Interaction Science Consultant

Senior BI engineering scope: semantic models, performance, and reporting modernization.

Data quality scorecards, Customer 360 dashboards, retail analytics redesign, AI-assisted control tower PoC, and CTRM reporting architecture.

Adastra (Thailand)

Jul 2021 - Oct 2022

Data Engineer & BI Developer

BI delivery and platform: Cognos-to-Power BI migration plus data pipeline work.

Cognos-to-Power BI migration, multinational stakeholder coordination, scalable data pipelines, and inventory app development.

Business Computer Service Center (BCSC)

Mar 2020 - Jul 2021

Power BI Developer & BI Consultant

End-to-end Power BI delivery embedded with ERP clients.

End-to-end BI delivery for ERP clients across retail, manufacturing, and international development sectors.

M.I.S.S.Consult

Oct 2017 - Feb 2020

People Development Consultant

Pre-BI: HR assessment analytics and automated reporting (where the data work began).

HR assessment analytics, automated reporting, and learning program design for business improvement.