Brand Intelligence Pipeline — dbt + Modern Data Stack Built transformation layers delivering trusted KPIs for brand performance analytics, Cultural Authority Score, Resale Premium Index, drop sell-through rate with full lineage and documentation. Stack: SQL · dbt · Python · Warehouse

Commercial Analytics Pipeline — Batch + Scheduling Automated ingestion, validation, and orchestration for revenue intelligence workflows across digital commerce and brand partnership data.

Stack: Python · SQL · Airflow · AWS

IP-Governed ML Dataset Pipeline: Prepared clean training datasets and feature tables for brand classification and drop performance modelling with trade-secret boundary controls and licensing compliance built into the pipeline architecture.

Stack: Python · Pandas · SQL

ABOUT ME

Jude Christina Gaspard

builds analytics systems at the intersection of brand intelligence, IP data governance, and commercial measurement, transforming behavioral, marketing, and operational data into structured insight systems that drive decisions at enterprise scale.

Her focus is analytics-ready datasets, KPI frameworks, and reporting layers built for industries where data carries legal and commercial weight — luxury, entertainment, digital commerce, and intellectual property. She designs pipelines that are not just fast and reliable, but governed: trade-secret boundaries enforced, licensing obligations tracked, royalty-ready outputs delivered.

With a formation in analytics engineering, IP law, and luxury business strategy, she brings a rare combination to data infrastructure the technical precision to build it right, and the domain depth to understand what it protects.

“Raw data carries value, risk, and intent.
A reliable pipeline decide what survives the journey. — intact, constrained, and usable..”

Technical Scope

• Analytics Data Models & KPI Layers

• BI & Reporting Structures

• Marketing & Interaction Data Pipelines

• Analytics-Ready Data Layers

• Applied AI Analytics Workflows

Skills

Languages

• Python

• SQL

Data Engineering

• ETL / ELT

• Data Pipelines

• Data Modeling

• Data Validation & Quality

Platforms

• Cloud Data Platforms (AWS / GCP / Azure)

Engineering Practices

• API Integration

• Git / Version Control

GenAI

• Generative AI (RAG)

Analytics datasets & Measurement pipelines

• Reduced manual reporting effort by ~35% through reusable data pipelines and transformations

• Improved dataset consistency and reliability by adding validation and standardized data models

• Cut analysis turnaround time by ~25% by restructuring data preparation workflows

• Increased data reuse across teams by introducing shared, well-defined data structures

Digital Assets & IP Operations

• 50+ digital assets (masters, metadata, behavioral signals) operationalized through analytics pipelines supporting valuation, forecasting, and controlled exploitation.

• 15+ proprietary datasets and internal models classified and routed through secured pipelines, preserving trade-secret boundaries across analytics and ML workflows.

• 120+ copyright and licensing assets moved through governed data pipelines, enabling catalog analytics, usage tracking, and royalty-ready reporting.

• IP-aware data architecture designed for environments where what the pipeline carries determines legal obligations downstream — not just data quality

Real Data

True Data

"I architect Unified Data Estates that bridge the gap between product craftsmanship and commercial intelligence. By leveraging Agentic Data Engineering and Vision-Infused Pipelines, I transform raw signals like RFID lifecycle events and unstructured textile metadata into executive-grade intelligence. My goal is to ensure that a brand's data architecture is as precisely tailored as their garments, driving Omnichannel Unity and Inventory Velocity at scale."

Thank you for your time, let's stay in touch