Source-controlled semantic models, validated DAX, and review-gated deploys — so dashboards stop breaking when the model changes.
8+
Years
BI and analytics consulting.
90s→7s
Measured result
Dashboard latency reduction.
PBIP/TMDL
Workflow
Source-controlled model review.
4
Microsoft certs
Credential links where public-safe.
Start here
Hiring review, consulting problem, or peer-level technical proof.
Recruiter / hiring
8+ years, Microsoft credentials, stack coverage, and proof points in a short scan.
Review resume & proof
Audience path
Consulting buyer
Reporting backlog, slow dashboards, fragile models, or governance visibility before a scoped call.
Discuss a BI problem
Audience path
Engineer / peer
Public PBIP/TMDL workflow evidence and source-backed validation logic.
Inspect public repo
Audience path
Visual proof
A code-native view of the delivery pattern behind the proof: source control, review, validation, deployment, and monitoring.
BI as production code
The workflow stays intentionally ordinary; the discipline is never skipping the review and validation gate.
Selected Work
Anonymized, evidence-led examples: public validation workflow, measured performance improvement, modernization, and governance visibility.
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.
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.
A Canadian retail client relied on 10+ legacy Cognos reports for sales and inventory tracking that were difficult to maintain and extend.
Outcome signal
10+ reports migrated
What this proves
Shows enterprise reporting modernization, migration experience, and comfort with operational reporting.
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.
A retail analytics platform needed a more maintainable reporting-layer design and better performance under concurrent usage as analytics adoption expanded.
Outcome signal
One reporting path: 25M to <100K rows
What this proves
Shows architecture-level performance work with rollout-readiness evidence, not dashboard polish.
Applied AI builder work
Recent OpenAI event work focused on the part that matters in real operations: messy data intake, validation gates, agent/tool controls, human review, and decision-ready handoff.
Sea x OpenAI Regional Codex Hackathon
A low-code harness layer for operational agents: trace the run, check eval and policy signals, route fallbacks, and keep MCP/tool access inspectable.
What this proves
Demonstrates Codex-enabled build speed with the discipline that matters after the demo: controls, fallbacks, traceability, and human review.
OpenAI x DataKind AI Skills Jam for Disaster Management
A disaster-response workflow for messy files: profile the data, expose evidence gaps, recommend the next dashboard view, and produce a handoff stakeholders can review.
What this proves
Demonstrates the bridge between BI quality, operational data judgment, and AI assistance that stays reviewable instead of pretending to be autonomous.
Trust signal
“bridge backend data engineering with front-end reporting”
Reviewer Y.L.
Senior Manager | Data engineering and reporting project
Services
Reporting modernization, safer semantic-model changes, measurable performance improvements, and governed BI delivery.
Service theme
4 to 12 weeksLegacy reports are fragile, hard to maintain, or built on platforms the team is moving away from.
Fit criteria
A platform migration is planned or stalled and reports are in scope
Service theme
2 to 8 weeksSemantic 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.
Fit criteria
Model changes have caused production issues that were only caught after deployment
Service theme
2 to 4 weeksDashboards load too slowly for daily use.
Fit criteria
A business-critical dashboard is too slow and the team has already tried the obvious fixes
Service theme
4 to 10 weeks for the initial scorecard; ongoing refinement separatelyData quality issues are invisible until a stakeholder notices something wrong in a report.
Fit criteria
Governance reviews lack a shared, data-backed view of quality across domains
Service theme
2 to 6 weeks for a POCThere 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
Not a fit when
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.
About
Senior BI delivery across Power BI, Databricks, SQL, Python, and Azure, with an engineering-led approach to reporting quality.
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.
Latest speaking engagement: Mar 2026 PBIP session
Credentials
Microsoft credentials remain separate from delivery claims; the proof sections above carry the work evidence.
Microsoft certification set
Public credential links are preserved on the resume page where available.
Public repository
A public PBIP/TMDL workflow showing validation logic, source structure, and engineering patterns peers can review directly.
Automated DAX measure testing built on PBIP, Python, and AI-assisted tooling, designed to catch calculation risk before deployment.
Pattern to inspect
Inspect how the repository organizes PBIP source files, semantic model parsing, and validation candidate generation.
Core Competencies
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
Beyond dashboarding alone: Power BI, SQL, Python, Databricks, semantic modeling, and reporting delivery across enterprise environments.
Proven work in dashboard tuning, data-quality visibility, and reporting reliability, including a documented improvement from 90 seconds to 7 seconds.
PBIP, TMDL, PBIR, validation-oriented workflows, and AI-assisted measure-testing practices that go beyond what most postings explicitly ask for.
Strong end-to-end ownership and solution-shaping evidence today, with broader architecture and mentoring scope still developing.
Contact
Whether you need reporting modernization, semantic-model engineering, or performance work, start with a short conversation.