% Generated by scripts/pdf-to-latex.py from site/resume.pdf — do not edit by hand. \documentclass[11pt]{article} \usepackage[margin=0.75in]{geometry} \usepackage[T1]{fontenc} \usepackage[utf8]{inputenc} \usepackage{lmodern} \setlength{\parindent}{0pt} \setlength{\parskip}{0.6em} \pagestyle{empty} \begin{document} Patrick Browne\\ Data Leader \& Architect 719.338.4035 | Colorado\\ patrickbrowne@outlook.com | pbrowne.net LinkedIn PROFESSIONAL SUMMARY Seasoned big data leader, engineer, and architect with over 15 years of experience. Prioritizes a progressive,\\ systematic, and results driven approach to AI. Excels at solving large-scale technical and strategic problems and\\ thrives in fast-paced environments with limited resources. A data-driven decision maker who constantly seeks\\ out innovative and disruptive opportunities, values candid feedback, and encourages lateral thinking among\\ colleagues. Emphasis on continuous learning and mentoring within a highly collaborative environment. SKILLS \textbullet{} AI/LLM/ML Development \& Implementation\\ \textbullet{} Enterprise Data Platform Architecture\\ \textbullet{} Revenue Forecasting \& Cost Optimization\\ \textbullet{} Data Org Management and Structuring \textbullet{} Product Roadmapping and Development\\ \textbullet{} Project Management \& Agile Leadership\\ \textbullet{} Data Governance Systems\\ \textbullet{} Hybrid \& Native Cloud Best Practices WORK HISTORY Sovrn Holdings Inc. - Boulder, CO 2016 - Current\\ Senior Manager, Data Engineering (2024 - Present) \textbullet{} Transforming development workflows by leveraging agentic AI, MCP, and LLM across multiple teams\\ \textbullet{} Leading a multi-team Data Engineering org, encompassing product, platform, and internal teams\\ \textbullet{} Driving cross-organizational product initiatives to develop industry leading offerings leveraging AI\\ \textbullet{} Forecasting revenue and operational cost, including continual cost optimizations\\ \textbullet{} Serving as a technical leader for Data Governance and Compliance\\ \textbullet{} Mentoring managerial staff and multiple teams with diverse objectives under Data Engineering\\ \textbullet{} Serving as the key contact for internal and external stakeholders Manager \& Staff Engineer (2022 - 2024) \textbullet{} Owned of multiple observability platforms for AI/LLM and Big Data/Data Analytics workflows\\ \textbullet{} Architected and administered an AWS zero-trust multi-tenant organization\\ \textbullet{} Designed a streaming backend capable of handling 20GB/s, processing over 250 billion records daily\\ \textbullet{} Managed internal and external batch processing ETLs using Spark\\ \textbullet{} Mentored and led projects, ensuring documentation, process improvement, quality standards, and on-time delivery \textbullet{} Led the support for frontend, backend, and middle-ware cloud migration from on-prem to AWS Senior Software Engineer (2020 - 2022) \textbullet{} Architect, and mentor for serverless, containerized, and big data storage, ingress, and egress\\ \textbullet{} Championed and mentored junior team members in best practices Software Engineer II (2018 - 2020) \textbullet{} Key contributor in AWS migration and development of Data Science processes\\ \textbullet{} Administered cloud and on-prem infrastructure for streaming and big data Senior Software Developer in Test (2016 - 2018) \textbullet{} Test automation lead for a Data Platform team of 9 developers\\ \textbullet{} Designed and maintained ephemeral system and integration test suites Dish Networks - Denver, CO 2015 - 2016\\ QA Test Engineer II \textbullet{} Lead Quality Assurance for the Data Architecture team\\ \textbullet{} Designed and maintained front-end Java-based test automation framework Drobo/Connected Data - Santa Clara, CA 2012 - 2015\\ QA Test Engineer \textbullet{} Lead tester for the launch of the Transporter private cloud storage device\\ \textbullet{} Lead tester for Drobo storage devices, both enterprise and commercial KEY PROJECTS Leadership \textbullet{} Organizational restructuring and alignment for backend and data departments\\ \textbullet{} Extensive experience in managing multi-team and geographically diverse groups\\ \textbullet{} Hands on influence on go-to-market strategy, technical implementation, and product roadmap\\ \textbullet{} Led a product-driven team for data wholesale offerings, managing 9+ offerings with a 55+\% gross profit margin and generating \$12M in annual revenue \textbullet{} Led Data Platform teams ingestioning, governing, and storing over 4PB of data per week\\ \textbullet{} Led Data Science, Data Analytics, and other “data-centric” teams\\ \textbullet{} Experience in contract negotiation and strategic forecasting using metrics-driven industry research\\ \textbullet{} ROI evaluation systems across products in AWS, Databricks, Snowflake, and other platforms\\ \textbullet{} Served as the point of contact for government and consumer data compliance AI/LLM/ML \textbullet{} End to end AI Agent workflow from JIRA ticket to PR and production deploy.\\ \textbullet{} Organization wide agent Context and MCP layer with dreaming compaction and Claude plugin\\ \textbullet{} Vectorized RAG implementation of canonical ID segments with entity resolution and autodidactic reinforcement \textbullet{} Architect for an AI LLM powered web content categorizer for audience and deal packaging for adtech\\ \textbullet{} Engineering lead for lookalike audience modeling, unifying disparate datasets Project Management \textbullet{} Led large-scale migrations and refactor efforts across the company, delivering projects on time and within budget \textbullet{} Evolved the full-tech stack of the adtech backend, serving both internal and external stakeholders\\ \textbullet{} Migrated of 60 node on-prem data lake and legacy data analytics architecture into native cloud system\\ \textbullet{} Implemented ground up tech adoption for both Snowflake and Databricks Data Governance/Admin \textbullet{} Implemented OPA and a single service layer to unify access to multiple products lines \textbullet{} Created a canonical ID system creation and implemented an identity graph\\ \textbullet{} Managed a 28 node, 42+ TB Cassandra cluster in AWS for customer-facing dashboard\\ \textbullet{} Admin of Pinot Startree cluster and ETL serving frontend dashboards\\ \textbullet{} Served as an admin and architect for Snowflake, Sagemaker/EMR, Databricks Cost Savings \textbullet{} Led upstream and downstream optimizations to reduce infrastructure costs, including application, storage, governance, and business process workflow optimizations. Includes multiple \$500k+ efforts. \textbullet{} Served as a key contributor in quarterly spend and revenue planning \textbullet{} Tracked and forecasted cloud costs across AWS and GCP organizations and supplementary services\\ \textbullet{} Implemented automated metrics to expose cost-to-revenue ratios across product lines and platforms Observability \textbullet{} ETL and data quality and efficiency monitoring for peta-byte scale ingest and egress workflows\\ \textbullet{} Developed data science centric job monitoring and analytics platform solutions\\ \textbullet{} Conducted streaming checkpoint and lag analysis for real-time and batch process signaling Streaming/ELT \textbullet{} Modular Spark Structured Streaming framework ingesting approximately 250 billion records per day across various teams and products \textbullet{} Modular Flink streaming application to Delta Lake, processing 9 GB/s and 200 billion records daily\\ \textbullet{} Adtech audience products with Databricks, AWS, Snowflake and GDPR/CCPA data compliance\\ \textbullet{} Spark in Snowflake, Databricks, and AWS for critical financial data and external data products\\ \textbullet{} Serverless event-based ingest monitoring and auditing system for disparate data sets and workflows\\ \textbullet{} Migration and greenfield data processing applications with k8s, wired for auto-scaling in EKS\\ \textbullet{} Kafka Connect ingestion from bare metal, geo-dispersed servers to S3 Infrastructure \textbullet{} Architect of AWS zero-trust multi-tenant organization and structure for Data Platform\\ \textbullet{} Developed Kubernetes cluster with applications and pipelines for big data use cases\\ \textbullet{} Created and managed on prem and cloud servers, applications, and databases with infra-as-code TECHNICAL SKILLS Frameworks \textbullet{} AI Agentic, MCP, Spark, Spark Structured Streaming, Flink, Lambda/Serverless, Glue, Kubernetes, Kafka Connect, Event-driven, APIG/ALB, REST, FastAPI, ORM, OPA, Jinja2, Structured and Unstructured data Databases/Storage \textbullet{} Vector DB’s, Databricks, GCS, Snowflake, EMR, Aerospike, Cassandra/ScyllaDB, Pinot/Startree, MySQL, Postgres, DynamoDB, RDS, Redis, Hadoop, MapR, Hive, Looker, Hue, Zeppelin, EBS, on-premise Linux Languages \textbullet{} Python, Go, Bash, Java, Scala, Rust Infrastructure \textbullet{} Docker, AWS Networking (VPC, IG, TGW, R53, ALB, etc.), EKS/Kubernetes, Glue Data Catalog, EC2, Airflow, Kafka, Kinesis, SNS, SQS, EventBridge, DataDog, Graphite/Grafana, VictorOps, Jenkins, GoCD, Vagrant, VirtualDock, Terraform, Terramate, Ansible, Helm, Cloud Formation AI/LLM \textbullet{} Harness Engineering, Claude, Cursor, Codex, LoRA tuning, LangFlow, LangGraph, RAG analysis, Lookalike Modeling, Traffic Shaping, Agentic workflows, Ollama, LangChain, PyTorch, TensorFlow Creighton University | B.S. in Business Intelligence Analysis EDUCATION \& CERTIFICATES AWS Solutions Architect - Professional Level Databricks Lakehouse Architecture HOBBIES AI, Classic Car Restoration, Welding, Wine-making, Snowboarding, Outdoors, Piano, Electronics, Exercise \end{document}