I build data functions from zero, at every size, and the teams that keep them running.
Proven at every size: Fortune 100, AI unicorn, and PE-backed
At All Star I took a company with no shared definitions to a modern stack and conversational AI in under five months, and led the move off legacy BI.
Eqtble and Workday at Paradox. Integrity and Cross Country Locums at All Star. Each deal lands on one set of certified numbers, faster than the last.
Leaders ask questions in plain English and get certified answers in seconds. Most BI rollouts never reach a third of the company.
13 of 16 across three companies: all 4 at Paradox, 6 of 8 at T-Mobile, and 3 of 4 at All Star. And people have followed me from company to company.
Every executive KPI is defined once, owned by the business, and served the same way to dashboards, apps, and AI. 50 certified and counting.
Leaders agree on what the number means, edge cases included.
A business leader owns the definition. Data owns the build.
The steering committee approves it into the data dictionary.
Built a single time in the semantic layer. No spreadsheet copies.
Dashboards, apps, and AI read the same definition, by role.
net_daysWhether a leader opens a dashboard, a coaching app, or asks AI a question, the number matches. That's what ends the "whose number is right" meeting.
I ship production apps with Claude Code, from first prototype to the tool people use every day.
A live, two-way coaching app built for 320 people, from consultants to the C-suite. It reads certified metrics, captures what leaders enter, and cut leaders' 1:1 prep from 8 to 10 hours a week to seconds.
See it in action ›Production web apps, analytics apps on the semantic layer, AI that answers from certified metrics, and executive briefs.
How I'd start at your company, before I touch a single tool.
Meet every leader, learn how the business makes money, and find the one number everyone argues about. Get to know my team as people first.
Define and certify the metrics that run the business, and ship one visible win leaders can use right away.
Deliver a data and AI roadmap tied to revenue and margin, the team plan to get there, and what we'll stop doing.
The tools change. The structure doesn't. Get the data in, model it once, govern it in a semantic layer, and serve it everywhere people already work.
The semantic layer is the heart of it. Every metric is defined and certified once there, so dashboards, apps, and AI all give the same answer.
Strategy and tools only get you so far. The teams that win are the ones where people feel seen, trusted, and invested in.Laura Ha
How I built a live coaching app in Claude Code, on the same certified metrics as the CEO's reporting, and cut leaders' 1:1 prep from 8 to 10 hours a week to seconds.
Sales leaders told us they spent 8 to 10 hours a week pulling metrics together before 1:1s. Targets lived in Excel and changed every month. I built Hustle Hub in Claude Code so every leader opens to what matters, every number matches executive reporting, and AI writes the 1:1 prep from real data.
The company had just moved onto a governed data platform, so the numbers were finally trustworthy. But the people who needed them most, sales leaders and consultants, were still working out of dashboards built for analysts and spreadsheets they rebuilt by hand. The CRM vendor had promised a coaching view for months, and it hadn't arrived.
Before writing a line of code, I sat with sales leaders and asked them to walk me through their week. Three questions came up every single time.
Every screen in Hustle Hub answers one of those three questions. Anything that didn't, we left out.
Not a report you have to go read.
A coach that tells you what to do next.
Built on the same certified metrics as the CEO's report.
So the numbers never start an argument.
AI reads each person's real numbers and writes the prep: what to lead with, what to ask, and what to agree on. Anything AI wrote is labeled. Numbers and flags are calculated, never guessed.
Most sales dashboards only read data. Hustle Hub also writes: targets, team structure, and change history live in the app's own database and sync back to the warehouse, right next to the actuals they're measured against.
Every KPI comes from a certified metric. The app never invents its own math.
Postgres on Cloud SQL through Firebase Data Connect, synced back to the warehouse.
Leaders needed a coach, not more charts, and every number had to match executive reporting.
Cost: we own the front end and its upkeep.
CRM org data wasn't reliable enough to decide who sees what, and permissions depend on it.
Cost: someone has to keep the hierarchy current.
Targets carry forward until changed, so leaders stop retyping them every month and past attainment never shifts.
Cost: a more complex data model up front.
Hiding a menu item isn't security. Each person sees exactly what their role allows, even with a direct link.
Cost: every page took longer to build.
Leaders used it for a week, we fixed what they found, then Sales Enablement trained every consultant.
Cost: a slower launch, in exchange for trust on day one.
Sales saw the first demo and asked for it to go live. The CRM vendor's version never shipped. Ours did, built by one leader with Claude Code and a small team, on governed data the whole company already trusted.
Data & Analytics Executive · Builds data functions from zero to AI-ready
Dallas-Fort Worth · Remote or hybridlaura.halopez@gmail.comlinkedin.com/in/lauraha10
Data and analytics executive with 15+ years building data functions at every stage: a Fortune 100 company (T-Mobile), an AI unicorn acquired by Workday (Paradox), and a PE-backed platform (All Star). Takes organizations from scattered spreadsheets to governed, AI-ready data in months, and has led data through four acquisitions across two companies.
Trusted partner to CEOs, CFOs, and PE sponsors. Builds teams people follow: promoted 13 of 16 direct reports across three companies, and colleagues have followed from company to company.
PE-backed healthcare staffing company. Lead the data function, reporting to the Chief Product & Technology Officer, leading a team of ~10 across analytics engineers, analysts, and consulting partners.
Conversational AI recruiting platform (acquired by Workday in 2025) powering hiring for Unilever, McDonald's, Nestlé, General Motors, FedEx, and Lowe's.
Southwest Airlines (Market Research Analyst) · AT&T (Project Manager, Data Analyst) · Berkley Select (Business Data Analyst) · Red Bull North America (Analyst)