Senior BI Engineer | BI Consultant Bangkok, Thailand Scoped intake

Power BI, engineered like production code.

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

Start a consulting conversation See the proof first

Years

Data and analytics delivery, including 6+ years in BI engineering.

90s→7s

Measured result

Dashboard latency reduction.

PBIP/TMDL

Workflow

Source-controlled model review.

Microsoft certs

Public credential links where available.

Release pipeline · illustrative

Power BI delivery, under engineering control

Illustrative view of the public repository pattern; it does not represent live-run proof.

model change

source-controlled

PBIP / TMDL diff is text-reviewable

The semantic-model change can be reviewed as source before release.

validation gate

pre-deploy

DAX and model-risk checks before deploy

Measures and model structure are checked as a delivery pattern, not a live run claim.

performance / quality

reviewed

Performance and report quality reviewed

Use documented case-study evidence only, such as the 90s to 7s improvement already on the site.

release readiness

approval required

Review-gated promotion

Promotion waits on an explicit review step; no commit, PR, test-count, or runtime claim is implied.

illustrative · pattern from public repo

Start here

Choose the path that matches the decision.

Hiring review, consulting problem, or peer-level technical proof.

Recruiter / hiring

Confirm role fit, fast.

8+ years in data and analytics, Microsoft credentials, stack coverage, and proof points in a short scan.

Review resume & proof

Audience path

Consulting buyer

Match the problem first.

Reporting backlog, slow dashboards, fragile models, or governance visibility before a scoped call.

Discuss a BI problem

Audience path

Engineer / peer

Inspect the real thing.

Public PBIP/TMDL workflow evidence and source-backed validation logic.

Inspect public repo

Audience path

Selected Work

Where the engineering shows up in the work.

Three high-signal examples: source-controlled semantic model work, measured performance improvement, and governance visibility.

View all case studies

Portfolio-safe validation workflow visual with TMDL and PBIR inspection, DAX risk checks, and review gates.

Semantic Model Engineering BI 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.

Outcome signal

Public repo + Mar 2026 talk

What this proves

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.

PBIP TMDL PBIR

Anonymized retail performance tuning visual with dashboard panels, query paths, and speed improvement cues.

Performance Tuning Retail

Retail Dashboard Performance Tuning

A retail analytics dashboard used by five departments loaded in over 90 seconds, making it impractical for daily decision-making.

Outcome signal

90s to 7s average load time

What this proves

Measurable performance proof tied to daily operational use — not a lab benchmark.

Power BI SQL DAX

Anonymized data quality scorecard visual with governed status panels and validation indicators.

Governance Visibility Banking

Power BI Data Quality Scorecard

Governance stakeholders needed an interim way to monitor data quality across critical digital-banking and card platforms before a permanent governance tool was ready.

Outcome signal

Refresh cadence: Monthly

What this proves

Shows BI delivery tied to governance trust, secure-environment constraints, and a maintainable handover path.

There is interest in using AI to improve BI workflows, but it is unclear where AI adds real value versus where it introduces risk.

Fit criteria

The team is evaluating AI for BI use cases and wants a grounded, review-first perspective

Azure OpenAI Python Azure Databricks

View service detail

Not a fit when

The honest filter comes before the sales call.

Review fit criteria

Reporting modernization

The underlying data platform itself is being rebuilt. Modernize reporting on stable foundations, not during a platform migration.
Semantic model engineering and validation

Power BI Desktop isn't yet the core authoring tool, or the team has no appetite for source control discipline. Validation workflows assume an authoring baseline to protect.
Performance tuning and BI quality hardening

The underlying data model itself needs to be re-architected. Start with Semantic model engineering instead; performance tuning works best on a model that is worth tuning.
Data quality and governance visibility

A formal data catalog or quality platform is already the chosen investment. This work is interim visibility, not a replacement for Purview, Collibra, or similar platforms.
Practical AI-assisted BI workflows

The goal is a production AI feature with vendor-grade uptime and SLAs. This work is scoping and POC delivery, not production AI engineering.

Proof band

Proof worth checking before the call.

Compact signals only: a recommendation, inspectable source, and named third-party coverage.

Recommendation

“bridge backend data engineering with front-end reporting”

Reviewer Y.L.

Senior Manager | Data engineering and reporting project

Read recommendations

public repository powerbi_demo_PBIPxGHCopilot

Automated Measure Testing for Power BI

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

Pattern to inspect

PBIP source structure, semantic-model parsing, and validation candidate generation. Source reference: scripts/evaluate_pbip_for_testing.py.

PBIP Python TMDL PBIR

Open repository GitHub profile

Jun 2026 Applied AI field prototype

Applied AI coverage

Dashboard Copilot for Disaster Response

Covered in OpenAI's article on disaster-response teams in Asia.

Demonstrates the bridge between BI quality, operational data judgment, and AI assistance that stays reviewable instead of pretending to be autonomous.

OpenAI DataKind Decision support Data quality

Read OpenAI coverage Open project repo

Contact

Have a BI problem worth discussing?

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

Start a consulting conversation Review resume first