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Senior BI Engineer | BI Consultant

Power BI, engineered like production code.

Source-controlled semantic models, validated DAX, and review-gated deploys — so dashboards stop breaking when the model changes.

8+ years Bangkok, Thailand Power BI, Databricks, SQL, Python, Azure
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Anonymized BI engineering workbench showing semantic model, validation, source-control, and delivery-readiness cues.

Measured result

90s to 7s

Dashboard latency reduced through dataset and SQL redesign.

Workflow discipline

PBIP / TMDL

Source-controlled semantic-model change with validation-oriented review.

Credentials

4 Microsoft

Credential links are published where public-safe; delivery proof sits in work and repos.

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Pick the path that fits your goal: hiring review, consulting conversation, or peer-level proof.

Recruiter

Review role fit fast.

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Consulting client

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Colleague / peer

Inspect public technical proof.

Jump to public repositories and workflow evidence before reading broader case studies.

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Proof Snapshot

Proof recruiters and clients can scan quickly.

Delivery scope, measurable evidence, and practical BI credibility at a glance.

8+

Years in BI and analytics consulting

Across banking, retail, HR, and enterprise delivery.

90s to 7s

Verified performance improvement

Dashboard load time reduced through dataset and SQL redesign.

10+

Legacy reports modernized

Cognos sales and inventory reports migrated to Power BI Paginated Reports.

4

Microsoft certifications

Credential links are published where public-safe; delivery proof is separate.

Visual proof

How the PBIP / TMDL workflow holds together.

A code-native view of the delivery pattern behind the proof: source control, review, validation, deployment, and monitoring.

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.

Selected Work

Selected work that shows how the delivery holds up in practice.

Semantic-model engineering, reporting modernization, and measurable performance work across production BI environments.

View all case studies

Semantic Model Engineering

Power BI Automated Measure Testing with PBIP

Maintaining 10+ production Power BI datasets and reports alone made measure-level regressions easy to miss. Changes were reviewed manually, if at all, and confidence in deployment dropped as the models grew. The underlying issue: a `.pbix` opened and saved is a review black box — nothing compares the model before and after.

BI Engineering Mar 2026

Why it stands out

Shows a recent workflow built from real production-maintenance needs — PBIP + TMDL + PBIR as the foundation for treating Power BI like production code — with public proof through a GitHub repo and a Mar 2026 speaking session.

semantic-model validation pbip testing
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PBIP-MEASURE-VALIDATOR TMDL INSPECTION

  • SalesModel.Dataset/definition/tables/Sales/measures
    24 measures
  • [Total Revenue].dax
    clean
  • [MoM Growth].dax
    clean
  • [Forecast Accuracy].dax
    review
  • ComplianceReport.pbir/report/pages
    6 pages

Validation summary

247

Measures

0

Errors

2

Pending

Validation proof: Mar 2026 GitHub + speaker session

Anonymized trading analytics reporting visual with source cards, report stack, compliance panels, and validation checks.

Reporting Modernization

Trading Analytics and Compliance Reporting

A new CTRM platform needed specialist Power BI reporting for middle-office, compliance, finance, and trading users, with reliable data flows and near-real-time visibility across multiple report types.

What this proves

Shows specialist reporting delivery in a complex multi-party environment, including data-validation discipline and stakeholder sign-off rather than architecture discussion alone.

reporting-modernization trading-analytics compliance-reporting
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Anonymized analytics platform modernization visual with source cards, model layers, optimization panels, and validation cues.

Performance Tuning

Enterprise Data Platform Modernization

A retail analytics platform needed a more maintainable reporting-layer design and better performance under concurrent usage as analytics adoption expanded.

What this proves

Shows architecture-level performance work with rollout-readiness evidence, not dashboard polish.

performance-tuning data-model-redesign tableau
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Testimonials

Recommendations that reinforce the technical proof.

What colleagues and clients highlight: technical depth, delivery quality, and low-friction collaboration.

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“bridge backend data engineering with front-end reporting”

Senior Manager | Data engineering and reporting project

Technical depth that still translates across teams

“best practices in semantic modeling for Power BI”

Team Lead | Power BI project delivery

Model quality paired with reliable delivery

“high quality and with minimal reviews”

Senior Colleague | Project delivery feedback

High-quality output with low-friction review

Services

Problems I help teams solve.

Reporting modernization, safer semantic-model changes, measurable performance improvements, and governed BI delivery.

See all services
Power BI Paginated Reports

Reporting modernization

When this helps

Legacy reports are fragile, hard to maintain, or built on platforms the team is moving away from.

Reports that are structured for maintainability, not just delivery.

View service detail
PBIP TMDL

Semantic model engineering and validation

When this helps

Semantic models are edited directly in Power BI Desktop with no source control, no review step, and no way to catch measure-level regressions before they reach production.

Models that can be reviewed, tested, and deployed with higher confidence.

View service detail
Power BI SQL

Performance tuning and BI quality hardening

When this helps

Dashboards load too slowly for daily use.

Dashboards that load fast enough to be used in meetings and daily workflows.

View service detail
Power BI DAX

Data quality and governance visibility

When this helps

Data quality issues are invisible until a stakeholder notices something wrong in a report.

A shared view of data quality that governance stakeholders can actually use to prioritize.

View service detail

About

The consulting profile behind the work.

Senior BI delivery across Power BI, Databricks, SQL, Python, and Azure, with an engineering-led approach to reporting quality.

Charnrit Khongthanarat, senior BI engineer and BI consultant

I've spent 8+ years building BI for banking, retail, HR, and enterprise teams — long enough to watch “just one more measure” bring down a production report. The work I care about now is the opposite: semantic models under source control, DAX changes with an automated risk check, and deployments that a colleague can review before they ship. Power BI as engineering, not configuration.

  • 8+ years across banking, retail, HR, and enterprise consulting
  • Power BI, Databricks, SQL, Python, and Azure in day-to-day delivery
  • Semantic models, performance tuning, reporting modernization, and governance visibility
  • Comfortable with both technical teams and stakeholder-facing delivery
About and background LinkedIn profile

Speaking

Delivered talks on practical Power BI engineering.

Past community and conference sessions on semantic model engineering, reporting modernization, and validation workflows.

All talks and community

  • Mar 2026

    Power BI Testing, Validation Gates, and AI-Assisted Semantic Model Engineering

    Conference talk | 45 min

  • Feb 2023

    Modernize Operational Reporting with Power BI and Paginated Reports

    Conference talk | 40 min

  • Mar 2021

    Increase Time Efficiency on Your Power BI Project with Tabular Editor

    Conference talk | 40 min

Tools and Stack

Core tools and workflow patterns used in delivery.

Semantic-model work, data platforms, Python-supported workflow design, and Azure delivery.

Power BI / Semantic Modeling

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

Engineering Workflow / Validation

Git Source-controlled BI workflows Measure validation DAX risk checks AI-assisted review Python

Data / SQL / Databricks

SQL T-SQL PySpark Azure Databricks Delta Lake ETL / ELT

Azure / Platform / Delivery

Azure Data Lake Azure Functions Azure OpenAI Power Apps Governance visibility Stakeholder-facing delivery

Credentials

Microsoft certifications that support the stack above.

Resume and credentials
Power BI Data Analyst Power Platform App Maker Microsoft Certified Trainer Azure Data Fundamentals

Public repository

Public repository proof that the engineering process is real.

Validation logic and workflow discipline you can inspect directly in a public repository.

All projects
Conceptual Power BI measure-testing scoreboard with pass, fail, warning, and validation workflow panels.
Featured

Automated Measure Testing for Power BI

powerbi_demo_PBIPxGHCopilot

Automated DAX measure testing built on PBIP, Python, and AI-assisted tooling, designed to catch calculation risk before deployment.

Validation-first BI engineering: systematic DAX risk detection, repeatable test coverage, and CI-ready semantic model workflows.

PBIP Python TMDL
Open repository
GitHub profile

Core Competencies

Enterprise BI alignment without the usual reporting fragility

The strongest fit is where reporting delivery needs engineering discipline: semantic models that can be reviewed, performance issues that need measurable improvement, and BI workflows that should hold up after handoff.

The profile is strongest for teams that need Power BI, SQL, Python, Databricks, and Azure work connected to delivery quality rather than treated as separate technical tasks.

  • Power BI + DAX

    semantic model and reporting delivery

  • SQL + Python

    analysis, validation, and automation support

  • Databricks + Azure

    cloud data-platform delivery context

  • PBIP / TMDL / PBIR

    reviewable BI engineering workflow

Above baseline

End-to-end BI engineering

Beyond dashboarding alone: Power BI, SQL, Python, Databricks, semantic modeling, and reporting delivery across enterprise environments.

Above baseline

Performance and reporting trust

Proven work in dashboard tuning, data-quality visibility, and reporting reliability, including a documented improvement from 90 seconds to 7 seconds.

Above baseline

Semantic-model engineering mindset

PBIP, TMDL, PBIR, validation-oriented workflows, and AI-assisted measure-testing practices that go beyond what most postings explicitly ask for.

Growing edge

Architecture and leadership trajectory

Strong end-to-end ownership and solution-shaping evidence today, with broader architecture and mentoring scope still developing.

Contact

Have a BI problem worth discussing?

Whether you need reporting modernization, semantic-model engineering, or performance work, start with a short conversation.

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