Peter Gerhat

Peter Gerhat

Mobile Data Architect

Building Reliable Data Foundations at Scale

Your app collects data. But is your team actually making decisions with it?

Most mobile and IoT scale-ups have two teams shipping in parallel - mobile devs building features, a data team producing dashboards weeks later. Nobody owns the full picture. I close that gap.

From app instrumentation to data pipeline to the dashboard your CTO opens on Monday morning - one person, end-to-end, no handoff.

About Me

Data Science Go 2019 Conference

Most companies with a mobile app or connected devices collect data, but still make product decisions based on gut feeling. The reason is almost always the same: mobile engineering and analytics work in silos. Events are tracked inconsistently, pipelines are fragile, and dashboards arrive too late to matter.

I fix exactly that. With 10+ years spanning mobile engineering, data pipelines, and AI integration, at Vodafone building telemetry infrastructure at scale, and at Sprengnetter connecting field operations data to business decisions – I’m the single person who can both instrument your app and build the pipeline behind it.

No coordinating two freelancers. No handoff risk. Decisions from real data, in 4-6 weeks.

Experience

Technical Product Owner

Technical Product Owner

Software Developer

Product Planner

Application Developer

Software Engineer

Education

MSc Information Systems

MBA Business Analytics

BSc Software Engineering

What I Do (Services)

DATA & ANALYTICS AUDIT

You suspect there's more in your app data. In 1–2 weeks I map what you have, what's missing, and where the biggest untapped value is.

OFFLINE-CAPABLE FIELD APPS

Field teams work where connectivity is unreliable. I build React Native apps with on-device validation and full offline capability - no server dependency, no lost data.

MOBILE ANALYTICS SETUP

Your app fires events, but nobody trusts the data. I instrument from the source, build the pipeline to BigQuery, and deliver dashboards your team actually opens.

LLM/AI INTEGRATION IN MOBILE APPS

LLM features in mobile apps are booming. hardly anyone can do both (app + AI integration).

IoT / TELEMETRY PIPELINE & OBSERVABILITY

Device data lands somewhere but is never used. I build a clean pipeline with SLOs, alerting, and cost monitoring - from chaos to control.

ON-DEVICE / EDGE ML INTEGRATION

Your data team has a model. Nobody can get it into the app. I bridge that gap: production-ready, offline-capable, latency-optimized.

Not sure if your app data is telling you anything useful?

Start with a free 30-minute audit call.

I'll look at what you're currently tracking, where the gaps are, and what a complete pipeline would look like for your setup - no commitment, no sales pitch.

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