Principal/Staff Data Engineer (Big Data, ETL)

Joel Iglesias Vazquez

Napa, California ****.********@******.***

www.linkedin.com/in/iglesiasjoel https://www.kaggle.com/jiglesias PRINCIPAL / STAFF DATA ENGINEER

Experienced Data and Analytics Engineer with over 15 years of expertise in designing scalable ETL/ELT pipelines, optimizing complex data architectures, and managing massive-scale workflows. Proven track record of improving data processing efficiency by over 300% and building highly reliable, trusted data frameworks. Adept at handling multi- terabyte data layers and turning complex business rules into robust, automated systems. PROFESSIONAL EXPERIENCE

Meta Senior Data Engineer

March 2020 – April 2022 August 2023 – Present

• Faced with highly unstable and fragmented data sources that caused downstream analytical delays, we engineered a robust, automated end-to-end data ingestion framework using advanced Python and Airflow scheduling, eliminating manual interventions and increasing pipeline throughput by 30% across core engineering operations.

• Tasked with mitigating severe legal and regulatory compliance risks related to federal marketing audits, we architected comprehensive, multi-layered interactive dashboards to ingest and monitor sensitive metrics, establishing 100% data visibility and ensuring flawless FTC compliance verification.

• Due to extensive bottlenecks and long turnaround times for cross-functional data discovery; we designed and deployed a unified, centralized data framework and semantic layer, empowering Data Science and business partners to perform rapid self-service analytics while reducing ad-hoc query support tickets by 40%. Peacock Data Scientist

April 2022 – July 2023

• To address stagnant user engagement and the slow, manual execution of product experimentation cycles, we automated complex A/B testing analysis and dashboards, accelerating experimental velocity by 50% and measurably increasing content personalization accuracy.

Apple Data Engineer & Analytics

July 2019 – March 2020

• Confronted with massive, unruly data streams from high-profile Apple, iTunes, and Apple Card advertising campaigns that strained processing infrastructure, then designed and optimized highly scalable, fault-tolerant ETL pipelines that successfully processed billions of daily events with zero data loss and provided real-time attribution insights to marketing leadership. Comcast Senior Data Scientist, Manager Data & Applications August 2015 – June 2019

• Legacy processing frameworks were unable to keep pace with multi-terabyte scale set-top box stream data, delaying critical executive decision-making; we overhauled the underlying relational data models and introduced optimized, distributed computation practices resulting on accelerating core KPI processing speeds by over 300% and saving hundreds of computing hours.

• The marketing division lacked real-time, data-backed audience sentiment metrics to guide multi-million-dollar xad spend decisions; we engineered custom analytics ingestion tools utilizing the Twitter API and statistical modeling, which successfully optimized marketing spend strategies and culminated in the receipt of a U.S. Patent for "Programming Insights and Analysis System". Dattlas Data Analytics

March 2012 – July 2015

• Faced with erratic, shifting API structures across multiple social networks that threatened the continuity of market intelligence reports; we constructed resilient, decoupled data integration pipelines for Facebook, Twitter, LinkedIn, and Glassdoor APIs, ensuring 100% uptime for mandatory compliance reporting delivered to the Mexican Stock Market (BMV).

EDUCATION

Master's Degree in Mathematical Statistics and Probability – CIMAT, Mexico Bachelor's Degree in Mathematics – Universidad de Guadalajara, Mexico TECHNICAL SKILLS

Python, SQL, ETL/ELT (Airflow, dbt), Tableau, Cloud (AWS/GCP), Data Modeling, Statistical Analysis, Data Science Workflows, MLOps, Statistical Analysis, API Integrations, CI/CD Pipelines.

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