I'm Rob Ranieri — a Data & Analytics Executive who turns complex data into decisions that move businesses forward.

With 10+ years leading enterprise analytics across CPG and manufacturing, I sit at the intersection of technical depth and business strategy. I architect the infrastructure, build the platforms, and translate raw data into what the business should do — from the data pipeline all the way to the C-suite level.

This site is a live portfolio of that work. Every project below is built on real enterprise data, masked for confidentiality, and walks through both the technical solution and the business outcome it enabled.

10+ years across CPG & manufacturing

STEP 1 - Data Ingestion

Raw data arrives from dozens of sources — APIs, cloud databases, flat files, ERP systems, and third-party data providers. The first step is connecting to those sources reliably and at scale.

Illustration of digital data and information being filtered into a laptop, including images of gears, charts, a globe, target, shopping cart, calendar, magnifying glass, chat bubbles, and various icons representing technology and communication.

Icon of a cylinder representing a database with the text 'SQL' on it.

Sharepoint

SQL DB

Excel

$10M+ in estimated business savings

STEP 2 - ETL Pipeline Engineering

Data is extracted, transformed, and loaded (ETL) via engineered pipelines. This is where data is cleansed, standardized, joined, and shaped into a format the business can trust and is ready for end-reporting.

Flowchart of cheese production process including steps: skin milk preparation, pre-treatment, incubation, thermisation, separation, cooling, cream production, mixing, filling, and cold storage.

A red, three-layered, isometric geometric shape with open sides, resembling a 3D open box or a layered structure.

Databricks

Alteryx

Matillion

500+ actively managed users on a single platform

STEP 3 - Cloud Data Warehouse Storage

Cleansed data lands in a centralized cloud warehouse, serving as the organization's single source of truth and powering every downstream report and decision. All reports point back to this trusted source.

Illustration of a data server with interconnected data points showing data flow or network connection.

Google Analytics icon with magnifying glass and bar chart inside a blue hexagon

Snowflake

BigQuery

AMZ Redshift

STEP 4 - Visualization & Insight

Data is surfaced through reporting tools & dashboards designed for real business users — not data teams. Every report is built around a specific business question and optimized for adoption at scale.

Digital tablet displaying various blue colored data charts and graphs, including bar graphs, line charts, and pie charts.

A circular chart with 12 multicolored plus signs arranged around the circle, each linked to labels with descriptive text.

PowerBi

Tableau

D3

The goal is never reporting for sake of reporting. Every platform has directly informed decisions, enabled executive alignment, or driven measurable operational improvements.

Mindset

I think of myself as an interpreter.

Not in languages — but between technical complexity and business reality. Sales, finance, supply chain, and marketing teams think and speak differently from data and engineering teams. Most analytics projects fail not because of bad data or bad code, but because of that understanding gap.

Bridging that gap is where I provide value. I've spent a decade learning to speak both languages fluently — and every project on this site is the result of that translation.