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Jorge Navarro

Data Engineer

Summary

Highly accomplished Senior Data Engineer with 10+ years of progressive experience in designing, developing, and optimizing scalable cloud-native data solutions and MLOps pipelines. Proven expertise in building robust data pipelines with Apache Airflow, FME, and Palantir Foundry, and leading observability strategies (Prometheus, Grafana, Loki, OpenTelemetry) that significantly improve system reliability and incident response. Adept at full-stack development, cloud deployments (AWS, Azure, Kubernetes), and leveraging various DBMS (PostgreSQL, Snowflake, Oracle) to drive data health and empower data-driven decision-making. Completed Cloud DevOps Engineer and Data Engineering with AWS Nanodegrees from Udacity, complemented by Coursera's Machine Learning Specialization, strategically positioning for advanced roles in Artificial Intelligence, Machine Learning, and Robotics data infrastructure.

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Skills

Data & AI/ML Tools Database Systems Cloud & DevOps Core Languages & Frameworks Observability
Airflow, FME, Palantir Foundry, Spark, Pandas, Jupyter, TensorFlow, scikit-learn PostgreSQL, Snowflake, MySQL, Oracle, SQLite, Redshift, Cassandra AWS (S3, EC2, Lambda, Glue, Athena, Redshift, Route 53, Auto Scaling, Load Balancing), Azure, Kubernetes (AKS), Docker, IaC (CloudFormation, Terraform), CI/CD, Git, Linux Python, SQL, Shell, Java, C/C++, HTML, CSS, JavaScript, Flask, FastAPI, Typer, Bootstrap Prometheus, Grafana, Loki, OpenTelemetry, OnCall

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Professional Experience

Professional Member of Technical Staff, AT&T Inc., CA, JUL 2023 – APR 2024

Focus: Observability Engineering & Incident Response for Critical Systems

  • Spearheaded the development and implementation of a comprehensive observability engineering strategy utilizing Grafana, OnCall, Prometheus, and Loki, with exploration into OpenTelemetry, significantly improving system reliability and reducing downtime.
  • Engineered an end-to-end incident response pipeline integrating Grafana alert rules, OnCall's alert groups, and escalation chains, leading to a quantifiable reduction in incident resolution time, improved system uptime, and faster stakeholder notification.
  • Administered and contributed to the core Grafana management codebase (Python, Flask), enhancing email notification functionality and streamlining administrative tasks within Kubernetes (AKS) deployments.
  • Designed and developed advanced, dynamic Grafana dashboards for data, application, and infrastructure monitoring, providing improved data visibility and actionable insights for 5+ teams, accelerating issue identification and response.

Professional-Software Engineer, AT&T Inc., CA, JUL 2019 – JUL 2023

Focus: Scalable Data Pipeline Engineering & Microservices Development

  • Engineered and optimized critical end-to-end data pipelines using Apache Airflow, Feature Manipulation Engine (FME), and Palantir's Foundry, ensuring data health and comprehensive observability for high-volume mobility transport data.
  • Developed multiple complex ETL pipelines with FME and managed a significantly sized ELT pipeline within Palantir's Foundry, resulting in improved data accuracy, reduced processing time, and robust support for numerous critical business operations.
  • Led the design and development of a standardized, deployable Flask-based ReST API template, addressing cross-cutting concerns (authentication, authorization, logging, OpenAPI Spec) and including Kubernetes (AKS) deployment artifacts.
  • Leveraged the API template to launch numerous scalable microservices supporting critical network elements (e.g., anti-spam, mobility switch orchestration) and internal tools, accelerating API development and deployment while enhancing service reliability.
  • Centralized API documentation through the deployment of a Swagger UI instance, improving developer experience and API discoverability.

Professional-Network Planning Engineer+, AT&T Inc., CA, JUL 2017 – JUL 2019

Focus: Enterprise System Maintenance & Feature Development

  • Collaborated on the full software development lifecycle of the Engineering Rules Database (ERD), a legacy N-tier system (Ext JS GUI, Java business logic, Oracle DB), committing enhancements, fixes, and refactorings to improve stability and usability.
  • Implemented end-to-end CRUD functionality for network element capacity planners, directly supporting 15+ planners and streamlining hundreds of engineering rule updates, enhancing data consistency and availability.

Sr Specialist-Network Planning Engineer, AT&T Inc., CA, NOV 2013 – JUL 2017

Focus: Data-Driven Performance Reporting & Automation

  • Designed, developed, and implemented Key Performance Indicators (KPIs) and Key Capacity Indicators (KCIs) in utilization and performance reports using Logi Analytics, providing critical insights for proactive network adjustments and improved decision-making.
  • Identified and implemented mechanization opportunities, developing tools and an Engineering Rules Database (ERD) web service to automate repetitive tasks, resulting in a significant reduction in manual effort and improved data accuracy.

Capacity Engineer, AT&T Inc., CA, JAN 2012 – NOV 2013

Focus: Network Capacity Management & AutoCAD Automation

  • Managed physical capacity for networks and network elements, including physical space utilization and heat dissipation analysis in AT&T central offices.
  • Contributed to C#-based automation solutions for AutoCAD, streamlining design workflows and increasing efficiency and consistency.

Education

  • Masters of Science in Computer Science
    • Georgia Institute of Technology, Atlanta GA. expected 2027
    • Current GPA: 3.85 (4.00 scale)
  • Bachelor of Science in Electrical Engineering
    • California State Polytechnic University, Pomona, CA. June 2011

Projects & Certifications

  • Cloud DevOps Engineer Nanodegree, Udacity (Completed)
    • Key Skills/Projects: Cloud deployments (AWS, Kubernetes), Infrastructure as Code (CloudFormation), CI/CD pipelines, automated testing, monitoring (Prometheus, Grafana), logging, configuration management (Ansible). Focused on building robust, scalable cloud infrastructure and deploying microservices.
  • Data Engineering with AWS Nanodegree, Udacity (74% Complete)
    • Key Skills/Projects: Data Modeling (Relational, NoSQL; Cassandra), Cloud Data Warehousing (AWS Redshift), Data Lakes (S3, Glue, Athena), Big Data Processing (Spark, PySpark), Data Pipelines Automation (Apache Airflow). Includes building a data Lakehouse for ML model training.
  • Machine Learning Specialization, Coursera (Completed)
    • Key Skills/Topics: Supervised Learning (Linear/Logistic Regression, Neural Networks), Unsupervised Learning (Clustering, PCA), Decision Trees, Recommender Systems, Reinforcement Learning, Practical aspects of ML development. Utilized Python (NumPy, scikit-learn, TensorFlow).