Skip to content
Charnrit K.
Résumé Let's talk

Services

Start with the reporting problem.

Make reporting more reliable, models easier to change, and delivery easier to maintain. Practical Power BI help, with SQL, Python, Databricks, and AI assistance where the work calls for them.

Power BIDatabricksSQL and PythonAzureSemantic models
Tell me about your project See selected work
BI service themes diagram showing reporting modernization, semantic model validation, performance tuning, governance visibility, and AI-assisted workflows.

Where I can help

Start with the friction point. Open the scope and fit details for the area closest to your problem.

Reporting modernization

4 to 12 weeks

Legacy reports are fragile, hard to maintain, or built on platforms the team is moving away from. Stakeholders work around the reports instead of relying on them. Migration backlogs grow because no one has time to untangle the old logic and rebuild it properly.

Better outcome

Reports that are structured for maintainability, not just delivery. Stakeholders use the reporting directly instead of exporting to spreadsheets. The modernization backlog shrinks because each report is rebuilt with a clearer model and fewer dependencies.

Scope, fit, and engagement details

How I help

  • Cognos-to-Power BI migration with structure cleanup, not just visual replication
  • Operational and management reporting in Power BI and Paginated Reports
  • Report architecture decisions: what to rebuild, what to retire, what to consolidate

Good fit when

  • A platform migration is planned or stalled and reports are in scope
  • Existing reports are too fragile or complex for the current team to maintain confidently
  • Stakeholders are asking for new reporting but the foundation is not ready
Team fit
Solo BI lead or a 2 to 5 person BI / analytics team
Investment
Scoped per engagement

Not a fit when

The underlying data platform itself is being rebuilt. Modernize reporting on stable foundations, not during a platform migration.

Power BI · Paginated Reports · SQL · Azure

Discuss reporting

Semantic model engineering and validation

2 to 8 weeks

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. Changes are tested manually or not tested at all. As models grow, confidence in deploying changes drops.

Better outcome

Models that can be reviewed, tested, and deployed with higher confidence. The team catches issues before stakeholders do. Model changes are traceable and reversible, which makes the pace of delivery more sustainable.

Scope, fit, and engagement details

How I help

  • PBIP, TMDL, and PBIR-aware working methods with source control integration
  • Measure validation workflows and DAX risk detection before deployment
  • Structured review gates that make models easier to inspect and evolve safely

Good fit when

  • Model changes have caused production issues that were only caught after deployment
  • The team wants to adopt source control for Power BI but is unsure where to start
  • Measure logic is growing complex enough that manual testing is no longer reliable
Team fit
BI team of 2 to 8 with at least one Power BI developer already in place
Investment
Scoped per engagement

Not a fit when

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.

PBIP · TMDL · PBIR · Python

Discuss models

Performance tuning and BI quality hardening

2 to 4 weeks

Dashboards load too slowly for daily use. Users abandon them or fall back to static exports. The underlying cause is usually a combination of dataset design issues, unoptimized SQL, and DAX patterns that do not scale, but without profiling, the team is guessing at what to fix.

Better outcome

Dashboards that load fast enough to be used in meetings and daily workflows. Performance improvements that are measurable and specific, not vague claims of "optimization." The team understands what was slow and why, so the fixes hold.

Scope, fit, and engagement details

How I help

  • Dataset redesign and SQL optimization targeting measured bottlenecks
  • DAX and query-path review for dashboard responsiveness
  • Quality-focused delivery for business-critical reporting that cannot afford to be slow or unreliable

Good fit when

  • A business-critical dashboard is too slow and the team has already tried the obvious fixes
  • A dataset has grown large enough that refresh times or query times are becoming a blocker
  • Stakeholders have stopped trusting the reports because of performance or reliability issues
Team fit
Embedded with your BI lead or a small BI team
Investment
Scoped per engagement

Not a fit when

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.

Power BI · SQL · DAX · Azure Databricks

Discuss performance

Data quality and governance visibility

4 to 10 weeks

Data quality issues are invisible until a stakeholder notices something wrong in a report. There is no consolidated view of where quality problems exist, how severe they are, or whether they are improving. Governance conversations happen without shared evidence, so priorities are hard to align.

Better outcome

A shared view of data quality that governance stakeholders can actually use to prioritize. Issues are tracked visibly, not buried in tickets. Teams can show whether quality is improving over time, which builds trust and supports investment decisions.

Scope, fit, and engagement details

How I help

  • Data quality scorecards and governed reporting views in Power BI
  • Stakeholder-facing visibility into issue tracking, severity, and resolution trends
  • Reporting designed for governance decision-making, not only technical completeness

Good fit when

  • Governance reviews lack a shared, data-backed view of quality across domains
  • Data quality issues surface only through ad-hoc complaints rather than structured monitoring
  • The team needs to demonstrate data quality posture to leadership or compliance stakeholders
Team fit
Governance stakeholders plus a BI team of 2 to 6
Investment
Scoped per engagement

Not a fit when

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.

Power BI · DAX · Azure Databricks

Discuss governance

Practical AI-assisted BI workflows

2 to 6 weeks for a POC

There is interest in using AI to improve BI workflows, but it is unclear where AI adds real value versus where it introduces risk. Teams worry about hallucinated outputs, ungoverned prompts, or AI-generated logic that no one reviews before it reaches production.

Better outcome

A clearer picture of where AI fits into the team's BI workflows and where it does not. POCs that are scoped honestly, with review controls intact. The team can make an informed decision about what to invest in further, without pressure from overstated vendor claims.

Scope, fit, and engagement details

How I help

  • Proof-of-concept work with Azure OpenAI in analytics-adjacent workflows
  • AI-assisted scenario drafting and engineering support with human review gates
  • Honest scoping: identifying where AI helps, where it does not, and what guardrails are needed

Good fit when

  • The team is evaluating AI for BI use cases and wants a grounded, review-first perspective
  • An AI-assisted workflow has been proposed but no one has scoped the governance or review requirements
  • Leadership is asking about AI in BI and the team needs a realistic assessment rather than a demo
Team fit
Small team of 1 to 3 with a named review owner
Investment
Scoped per engagement

Not a fit when

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

Azure OpenAI · Python · Azure Databricks

Discuss ai workflows

Engagement shapes

The scope, duration, and handover are agreed before work begins.

Advisory review

A short engagement to assess the current state of reporting, models, or governance and recommend concrete next steps.

  • Review a semantic model for structural risks and validation gaps
  • Assess a dashboard performance problem and identify the likely root causes
  • Evaluate a modernization backlog and recommend a sequencing approach

Focused modernization sprint

Time-boxed delivery against a defined scope, typically a migration, a performance fix, or a governance visibility build.

  • Migrate a set of legacy reports to Power BI with structure cleanup
  • Tune a slow dataset and deliver measurable performance improvements
  • Build a data quality scorecard for governance stakeholders

Embedded delivery support

Join the team for a sustained period to deliver BI work alongside existing engineers, analysts, and stakeholders.

  • Hands-on Power BI delivery embedded within a data or analytics team
  • Ongoing semantic model engineering and validation workflow support
  • Cross-functional delivery across reporting, data engineering, and governance workstreams

Work behind the services

Examples of the technical decisions and delivery constraints involved.

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

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.

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.

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

Governance Visibility

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.

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

Read case study

Based in Bangkok. Ready to work remotely.

Bangkok, Thailand (UTC+7), with remote-friendly engagements across Asia and globally. Onsite work in Bangkok is possible when the scope benefits from being in the room.

Before a first conversation

Practical answers about fit, timing, and how engagements start.

Do you work remotely, or only in Bangkok?

Based in Bangkok, Thailand, and remote-friendly across Asia and globally for short engagements. Onsite work in Bangkok is possible when a specific scope benefits from being in the room.

How long is a typical engagement?

Advisory reviews run 2 to 4 weeks. Focused modernization or performance sprints usually sit at 2 to 8 weeks. Embedded delivery is longer, typically 4 to 12 weeks. Scope and duration are agreed before the engagement starts.

Do you work under NDA?

Yes. Most engagements involve sensitive client material and run under standard confidentiality terms. The case studies on this site are anonymized to industry patterns. No client names, production data, or credentials are disclosed publicly.

How quickly can you start?

It depends on what is already committed. Send a brief through the contact form; I review inquiries personally and reply with the next step. A realistic start window is agreed once the scope and availability are clear.

Do you only take Power BI work?

Most engagements center on Power BI because that is where the depth is. SQL, Python, and Databricks show up when the reporting problem extends upstream. The work is not positioned as full data platform architecture or data engineering lead.

Can recruiters and hiring managers use the same contact form?

Yes. The inquiry-type dropdown on the contact form routes hiring conversations, consulting work, advisory or embedded support, speaking, and peer outreach separately.

What does a first conversation usually look like?

A 30-minute call to hear the problem, the constraint, and what has already been tried. The goal of the call is a clear yes, no, or scoped proposal. If it is not a fit I will say so and suggest someone else when I can.

Contact

Tell me what is getting in the way.

A slow dashboard, fragile model, modernization backlog, or unclear validation process is a useful place to start.

Start a consulting conversation See my background