Software technology executive and hands-on CTO, SVP Engineering, chief software architect, and product innovator with an extensive history of leading large software engineering teams building cloud-base sophisticated PaaS systems. Technology architect in petabyte scale, event driven and micro-services centric systems. This includes real-time processing systems, big data analytics and ML/AI, adtech, martech, eCommerce, fintech, search, and cloud-based systems, using Agile/Scrum project management across the software life cycle. Currently leading a team of 85 including front end and cloud software engineers, data engineers, systems architects, data scientists and QA automation engineers. Successfully designed, architected and deployed sophisticated and scalable, clustered cloud systems, including both server and serverless microservice-architectures, using various AWS and Azure services and stacks, sophisticated clustering and auto-scaling, Java, JavaScript, C#, Node.js, Python, DynamoDB, Cassandra, Apache Spark, DataBricks, Data Lakes, Lakehouses, Graph databases (Neo4j), API Gateways, Docker/Kubernetes, streaming with Kinesis and Kafka, auto-scaling, Lambda, ML/AI, TensorFlow, and many other cloud services across several companies. History of building world class software engineering, data engineering, data science, QA and product management teams, providing both technology, process, and business leadership and mentoring across the entire product development life cycle. Able to work at all levels from C-level and board interaction down to the software development and deep architectural levels. Background in event-driven, serverless, scalable cloud SaaS and PaaS systems handing tens of billions events a day. Leading large, high growth engineering teams

• cloud engineers, front end engineers, data engineers, data scientists, QA, and systems architects. Long time use of AI/Machine Learning and modeling, Information Retrieval methodologies, real-time and batch analytics, Neural Nets, Big Data lakes and lakehouses. Highly experienced in agile development methodologies, with extensive use of Scrum, Kanban, Jira, and Confluence tools, sprints, backlog grooming, and CI/CD deployment. Led the team that designed, built, and patented the world famous "Mobile Deposit" used by hundreds of millions of mobile users worldwide. VP Engineering, North America 2020
• present Tealium Inc. Tealium is one of the largest and fastest growing cloud-based Customer Data Hubs, handling billions of events a day from over 1000 customers, and interfacing with hundreds of vendors, within an event-driven, real-time system.
• I run a North American team of 85 engineers within a company of 750. This includes back-end cloud engineers, front end engineers, data scientists, data engineers, and QA automation engineers.
• We work with 25 product owners and technical project managers on a large portfolio of cloud based product offerings, all of which are part of an integrated system PaaS.
• Technologies include Java microservices, Kubernetes, Kafka, DynamoDB, MongDB, Redshift, Clickhouse, React on the front end, machine learning with Spark, DataBricks, DeltaLake (a lakehouse) and an automated CI/CD environment across Jenkins, ArgoCD/Artifactory.
• We are an agile shop and utilize the INVEST for our definition of done, vis-à-vis our Epics and User stories. We run two week sprints across my 12 scrum teams, with key KPIs for tracking velocity, burndown etc.
• Our products are highly scalable and event driven. They span the use of microservices, SQL and NonSQL Databases, advanced Machine Learning pipelines using DataBricks and DeltaLake, pub/sub via Kafka, and cluster orchestration and automated scaling using Kubernetes.
• I work closely with the C-level executives on company strategy, through product portfolio priorities, down to the operational details of architectural design, data design, scalability, reliability as well as strong UI/UX approach to our customer experience.
• Led the tech debt initiatives around migration to Kafka, Kubernetes, CI/CD and automated QA infrastructure.
• Migrated the team to an emphasis on non-functional requirements around auto-scalability, fault tolerance, recoverability and reliability.
• Grew team from 45 to 85 engineers, while emphasizing KPIs and increased velocity of product deliverables.
• Hired, mentored and setup the machine learning/data science team, and drove the adoption of DataBricks and Delta Lake in order to adopt a machine learning pipeline for development of dozens of customized models for our customers, including fraud detection, propensity to purchase, churn predictions and others.
• Scaled the engineering leadership team with a focus on a technology vision and strategy, an emphasis on cross-team adoption of architectural standards, constant innovation in our products and scalability, and working close with the Production Operations teams on NFRs
• scalability, fault tolerance and recoverability.