The Power BI Whisperer | Power BI, Fabric Principal Consultant @Simple BI Inc

Let’s get to the point: you’ve got data problems, and I’m here to solve them. 
 
Imagine running an operation where every decision is backed by clear, actionable insights.  

No more guesswork.  
No more surprises.  

Just a seamless flow of information that drives your company forward. 

I'm David Giraldo, Principal Consultant of Data Analytics at Simple BI.  

In every corner of the manufacturing world, I’ve seen: 
 
• Unreliable demand forecasts 
• Constant stockouts that leave customers frustrated 
• Capacity constraints bottlenecking your production, eating into your profits  

Over the past few years, I've helped companies like Microsoft*, Westlake Chemical*, Waste Management*, MSA The Safety Co*, Sub-Zero Group*, Tricorbraun*, and Kroger Pharmacy* not just navigate these issues but turn them into strengths. 
 
Here’s how I solved them: 
 
1. We start with your data.  
 
Whether it’s buried in SAP, outdated systems or siloed in 1,000 Excel files across your organization, we dig in and bring it all together.   

From there, we design advanced analytics solutions using Azure Data Platform, Microsoft Fabric to give you a 360-degree view of your operations. 
  
2. We build clarity.  

We create reports in Power BI that deliver the exact insights you need to: 
 
• Optimize your supply chain 
• Predict demand more accurately 
• Manage your inventory in real time 
 
3. We drive action.  

We work with your team to automate processes and simplify operations so you can focus on what matters:  

Growing your business without the operational headaches. 
 
My goal is simple:  

I help manufacturing leaders turn data into decisions that don't just keep businesses running, but position them to thrive in an increasingly competitive market. 
 
Ready to see how your data can work as hard as you do? 

Let's chat. Send me a message here on LinkedIn or visit www.simplebi.net. 
  
*Through third-party

David Giraldo

I’ve advised against migrating to Fabric more times than I’ve advised doing it:

The hype hides the rebuilds. Before I move a single table, the team has to clear three ugly gates: 

1. Name the constraint in numbers 

• Pick one primary constraint: concurrency, latency, or cost. 

Examples: P95 page load > 3 s at 60 concurrent users. Daily ETL window > 2 hours. Compute spend > $600/day with no caching strategy. 

If you can’t quantify the pain, Fabric won’t fix it.  

2. Name the owner for 3+ months 

• One person, not a committee. They own migration, training, and comms. 

• Deliverables on day 0: 30/60/90 plan, training schedule by role, weekly comms cadence, cutover criteria. 

• If no owner, migration becomes a side quest. Side quests die. 

3. List what you’ll rebuild by name 

• Security model: RLS rules, workspace structure, service principals, key rotation. 

• Pipelines: which ADF/SQL Agent/Databricks jobs become Fabric pipelines or notebooks. List them: ingest_customers, ingest_orders, reconcile_finance, etc. 

• Downstream contracts: reports that break if schemas change, with owners. 

Rule: If you can’t name it, you’re not ready. 

The hardest gate is usually #2. Tech is “fun.” Ownership and comms are not. That’s where migrations stall, burn budget, and quietly boomerang back to the old stack. 

I keep a one‑page “Fabric Decision Tree” to run this discussion in under 30 minutes and surface the blocker fast. If the blanks stay blank after that meeting, we postpone and fix the basics first. 

PS. Which gate blocks you right now?

Saved over $500k for clients with 25+ reporting and data analytics solutions | Principal Fabric Analytics Consultant

Open access can feel like empowerment. In practice, it’s how reports break. You’ve likely lived this:  

A well‑meaning edit to a shared measure makes revenue look 4% light.  

Teams fills with questions.  

Finance pauses the close for six hours.  

Trust takes much longer to repair. 

To prevent that, here's my simple guardrails test you can share with your team: 

If it’s a strategic KPI → certify the dataset, write down the definition, and only allow view access to the dataset with role-based access.

If it’s exploration → keep it in a separate sandbox workspace or personal workspace. 

If a report is promoted → name a single owner and add a visible “What’s new” panel with release notes. 

If usage drops → alert the owner (not “all hands”) and decide whether to fix, deprecate, or archive. 

Two helpful policies: no owner, no publish. No edits on certified content. 

Self‑serve should feel safe and fast: access, with governance built in. 
 
Which one guardrail would prevent most of your fires? Choose it and implement it this week.

Fabric rollouts don’t fail in month six. They fail in week two:

Night before sprint 1, Teams lights up with: 

“Which workspace are we using?”  

“Lakehouse or Warehouse?”  

“Who owns the semantic model?”  

The Friday demo is still on. Everyone’s tense. 

I fix it with a two‑week kickoff that locks the basics: 

• 20‑minute intake to agree on role, timeline, and decision rights 

• Lakehouse vs Warehouse split decided and written down 

• Workspaces and permissions set for Dev, Test, Prod 

• Semantic model stubbed with grain and conformed dimensions 

• Guardrails set: certified datasets, refresh windows, named owner, visible change log 

This is what my team does, white‑label under your brand. 

Your email. Your standups. Your deck. 

We get the blueprint right. Your team ships. We unblock quietly. 

Need hands on a current project? DM me and we can start next week under your brand.

Everyone loves the final dashboard. Nobody sees the trade-offs: 

It’s 10 p.m.  

The board meets at 9.  

A VP messages: 

“Can we add one more slice?” 

Meanwhile a key visual hangs because the model is working too hard.  

You fix it, keep the story intact, and pray nothing else tips over. 

Here’s what “fast” in Fabric actually took: 

👉 We settled 7 definitions up front in the semantic model so every page told the same truth. 

👉 We killed a 14-minute DAX by pushing the math into the Lakehouse and materializing a clean Gold table. 

👉 We moved one page from DirectQuery to Import to stop chatty, slow queries. 

👉 We dropped a pet visual so every view stayed under a 2-second target. 

👉 We cut hover-only details execs never use and agreed on scope at 10 p.m. instead of pretending it’s “quick.” 

My rule on Fabric builds: earn speed with choices first. 

Right storage, right model, right visuals. Then add back only what proves its value. 

Speed is bought with choices, not magic. 

PS. What percent of your “Fabric work” is actually plumbing? DM me if you need a hand fixing it.

Do you need Microsoft Fabric or just discipline? 

Here’s the no‑nonsense test I run with clients. Say yes to at least 2 before you invest: 

→ You combine 3+ independent source systems regularly (ERP + CRM + ops logs).  

→ You need consistent history beyond 12–24 months (point‑in‑time, slowly changing definitions). 
 
→ Analytics is slowing operational systems (timeouts, throttling, angry admins).  

→ You have ML/advanced analytics that need raw detail and governed features.  

→ You have real governance needs: row/column security across domains, audit trails, retention.  

If you checked fewer than 2 boxes, keep it simple:   

→ Stage only the tables that drive top decisions. 

→ Build one curated semantic model for the top 20 metrics. 

→ Align refresh to decision cadence (daily/weekly, not “as often as possible”). 

→ Publish definitions and owners next to the KPIs. 

→ Offer a clean “Export with context” (timestamp + definition notes) for Excel users. 

When Fabric shines, it’s obvious:  

→ Multiple teams producing shared, governed data products. 
→ Cross‑domain lineage and controls you can’t fake with spreadsheets. 
→ Near‑real‑time feeds that actually trigger actions. 
→ Heavy batch + streaming + notebooks you want under one roof. 

Platforms amplify your habits. Good habits make any stack work. Bad habits just get more expensive at scale. 

Be honest: how many boxes did you check today?

Start every Fabric project with the same four assets: 

1. Data product catalog

→ What exists 
→ Who owns it 
→ Who can use it 
→ SLA 
→ Lineage 
→ Request path 

One list that stops random datasets from multiplying. 

2. Model template 

An opinionated semantic model pattern:  

→ Clear grain 
→ Reusable measures 
→ Naming rules 
→ Time logic 
→ Row-level security 

So logic lives in the model, not in visuals. 

3. Refresh policy 

Schedule tied to decision cadence, failure alerts with an owner, and a visible cost per refresh.  

You stop over-refreshing and start protecting the critical jobs. 

4. Owner map 

Named owners for the top metrics, workspaces, and pipelines.  

Definition page, change log, freeze windows during critical periods.  

No owner means no ship. 

With these in place, “one more view” takes hours, not weeks. Without them, every page is a risk disguised as progress. 

How we deliver it white-label on Day 1: 
 
• Your brand, your channels, our specialists. 

• A focused kickoff to rank the top 20 metrics and data products. 

• We drop the catalog, model template, refresh policy, and owner map into your repo and workspace. 

• You take the credit. Your client gets a clean start. No extra headcount. 

Platforms amplify habits. The Starter Kit gives your team the right habits on day one. 

Want this in your next proposal under your logo? DM me.

Someone asks for “one more view” right at the end of the sprint. Here’s the problem: 

Nobody budgets the work that makes that view safe to trust.  

Here’s where the work actually goes before visuals:  

• Data model – 50%   

One semantic model, clear grain, reusable measures. If logic lives in visuals, you’re building debt.  

• Lineage and reliability – 20%  
 
Source-to-metric map, refresh cadence, alerts, owner. If you can’t trace it, you can’t fix it. 

• Metric definitions – 20%  

Plain‑English meaning, formula, examples, effective date, change log, accountable owner.  

This kills definition drift. 

• Visual layer – 10%  

Layout, colors, filters. Polished last because polish multiplies risk if the core is shaky. 

Before I ship “one more view,” I require three artifacts: 

1. Model snapshot 

A simple diagram and the list of measures with a one‑line purpose each. 

2. Lineage card 

Sources, refresh schedule, failure alerts, and who to call when it breaks. 

3. Definition sheet 

Top 20 metrics with meaning, formula, owner, and last changed date. 

No new page if any of the three is missing. If stakeholders still want it, we agree on the risk and a deadline to shore up the foundation first. 

For a 20‑hour request, I’ll spend 16 on model, lineage, and definitions. The last 4 on visuals. That’s how dashboards survive next month’s schema change. 

If you want adoption, fund the foundation first.

I stopped handing off deliverables. I hand off ownership: 

The difference shows up about 90 days after the project closes. 

A deliverable without an owner has a half-life: the definitions drift, the model falls behind, and when the dashboard breaks on a Monday, nobody knows who to call. 

So the first conversation with any new client isn't about scope. It's four questions: 

Who owns this domain when I leave? 

Who approves a change to the model? 

What happens when a refresh fails? 

Who documents the next update? 

If those don't have answers, we answer them before I write a single line of DAX. 

The handoff is a working system: named owners, documented rules, a clear recovery path. 

If nobody owns it when I leave, I didn't deliver. I created a dependency.

Most staff‑aug fails because the contractor sits outside the team. I refuse that model. I embed: 

Inside your standups, your Slack or Teams, your backlog, your release cadence. 

Day 0: access, owners, acceptance criteria. Named VP question. Named metric owner. P95 target. Data freshness SLA. 

Day 5: first landing view that passes the 10‑second test for the VP. One screen, one story, zero guessing. 

Week 3: load tested at 60 concurrent users. “What changed this week” summary at the top. 

Week 6: >= 70% of target users active weekly. 0 failed refreshes in the last 14 days. Handover runbook started. 

Week 9: runbook finished. Support queue under 5 open items older than 7 days. Cutover criteria met. 

Your team builds. I make the outcomes visible and unblock fast. The goal isn’t a hero developer. The goal is a system that survives after we leave. 

PS. Looking for a Fabric partner? DM me.

Fixing BI without a diagnosis is rearranging the debt. 

Three questions I run before I touch anything: 

→ Who uses this, and what decision does it support? 

If nobody can name a decision, the asset isn't broken. It's irrelevant. Fixing irrelevant things is expensive clutter maintenance. 

Skip this check and half the tickets on your backlog disappear. You just stop working on them. 

→ Who owns it after the fix? 

No named owner means the fix is temporary. It drifts back to broken within 60 days. 

Assign the owner before you assign the work. That's what makes a fix stick. 

→ What breaks downstream if I change it? 

One measure can sit inside 14 reports. Change it without checking - 14 reports now have a problem. 

Map the connections before touching the model. The blast radius is wider than it looks. 

Know the decision, name the owner, and map the dependencies. 

Then fix it.

KCIC

Senior Consultant

CME Group

*Engineered labor forecasting dashboard in Power BI and Excel with integrated data from a variety of sources.
*Led weekly meeting to demo recent updates to the labor forecasting dashboard.
*Created Entity-Relationship Diagram of all data sources and tables for CME’s migration to Google BigQuery and Google Looker.
*Helped portfolio managers with reporting needs in Power BI and Excel.
Performed data validation, investigated data discrepancies and partnered with stakeholders to resolve root cause.

Portfolio Management Data Analyst Consultant

West Loop Strategy

Client Responsibilities
*Translated client business requirements into creative, technical solutions using AWS’s BI tool.
*Engineered over 50 dashboards for 7 clients across different industries to drive better business decisions with their data.
*Programmed SQL queries with technologies such as Amazon Athena to extract, transform, and load data into AWS’s BI tool, to allow companies to make sense of their data.
*Led the migration of 19 client reports of education data into AWS’s BI tool using SQL to expand the capacity of a client’s system.  
*Educated client IT team on how to embed Microsoft Power BI Dashboards in Sharepoint and how to establish and maintain user permissions.
*Contracted by AWS to answer Community questions on their BI tool to resolve data analysis issues.

Internal Responsibilities 
*Wrote documentation on various product offerings and designed marketing materials.
*Improved the West Loop Strategy website using webflow.

Data Analyst Consultant

Epic

Client Responsibilities:⁣
* Diagnosed, debugged, and documented critical software issues for two healthcare systems and resolved the root cause of gaps in functionality and existing issues in the software.⁣
* Reduced issue and project resolution timelines by introducing a weekly report of all software issues, enhancement/upgrade projects, and third-party integrations to be certain everything was documented and resolved. ⁣
* Programmed searches and created queries of patient data to understand the scope and priority of issues in the software. Familiar with InterSystems Caché and SQL.⁣
* Improved client IT teams’ efficiency and ability to resolve issues by leading education sessions on Epic data structures, application build and functionality, troubleshooting tools, building technical reports, creating searches, and changes to the software.⁣
* Discerned what code fixes and enhancements clients needed and partnered with R&D to ensure they were delivered to clients in a timeframe that minimized negative system impact and optimized available functionality.⁣
* Ensured client IT/Operations teams’ success of quarterly software upgrades, implementation of new features, and integration with third-party vendors. ⁣
* Created reports to provide insight into patient data and to help streamline client processes.⁣
⁣
Internal Responsibilities:⁣
* Educated and provided support to Epic engineers on dental application.⁣
* Co-produced a five-year development roadmap for Epic’s dental application.⁣
* Developed three voice commands for Epic’s dental voice assistant.⁣
* Designed and programmed (using HTML and CSS) an internal website containing information on dental application functionality, troubleshooting guidance, and resources.⁣
* Acted as secondary contact for vendor/customer relations and integration support for dental imaging.⁣

Technical Solutions Engineer

Peter Nolan Consulting

Developed customized websites using primarily HTML and CSS.

Freelance Web Developer

Eleven Fifty Academy

Attended an intensive coding bootcamp where I learned HTML, CSS, and JavaScript. Attached is a description of program curriculum. During this program, I attended an intensive two-day class on Agile, Scrum, and Kanban.

Smart Launch Tech

Lyons Township High School

My responsibilities included supervising a total of 15 goalkeepers and developing their technique, improving their knowledge of the position and improving the goalkeepers confidence. I also advised the varsity men's soccer coach on the goalkeepers abilities and where each goalkeeper would fit in the program. As a result, the top goalkeepers I recommended at the end of each summer to the varsity men's soccer coach were given a spot on the team.

Part-time Soccer Goalkeeper Coach

I am intensely curious, detail-oriented, and I have a passion for problem-solving. My ideal work environment is supportive and collaborative, with ample growth and learning opportunities.

My ideal role would allow me to utilize my passion for data analysis, research, and technical problem solving. I have years of experience collaborating with clients and in technical customer service roles. I am very comfortable working with Microsoft Power BI, Amazon QuickSight, and Excel. I know this experience will allow me to successfully transition to any other Business Intelligence tools. In addition, I thrive in an environment where I have the autonomy to be creative and also regularly collaborate with coworkers or clients to gain feedback. While working at Epic, I refined my ability to juggle and prioritize many competing tasks. I also have strong documentation skills and always persevere until a task is complete. 

My hobbies include hiking, photography, travel, and financial planning.

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