Lead expanded centralized Data Engineering group supporting business functions across Dropbox including Data Science, Product, Finance, Marketing, Experimentation, Customer Experience, and People Analytics- Improved the reliability of critical financial data pipelines powering forecasting, reporting, and strategic decision making. Established and executed a multi-year strategy to unify Dropbox line of business reporting into a single, modular system with low latency availability and consistent business definitions - Delivered improvements to experimentation analysis, marketing campaigns, machine learning models, and user behavior analytics- Partnered with Data Infrastructure to build foundational data management capabilities, reducing data pipeline development overhead and improved reliability- Managed a 30 person data engineering organization consisting of three data engineering teams with engineering managers and data engineers. Rapidly hired 10+ engineers and managers in <6 months to expand team to meet increased demand- Established frameworks for annual and quarterly planning as well as operational guidelines for data pipeline SLAs- Published a Data Engineering-specific career ladder as part of Dropbox's career framework (https://dropbox.github.io/dbx-career-framework/ic5_staff_data_engineer.html)

Lead multiple teams across Data Platform, including infrastructure, tooling, and data engineering, projects and responsibility have included:- Data Infrastructure, running a large-scale (10s of PBs) Hadoop data warehouse and services built on top powering analytics and distributed processing use cases. Led project to define and validate migrating entire infrastructure from on-prem to cloud- Analytics products, including data exploration and visualization, experimentation and feature gating, and data management- Building blocks for Dropbox infrastructure including queueing (leveraging Kafka), distributed storage, and key/value data stores- Building a centralized data engineering team from scratch and delivering the first gold analytical data models- Led unified event strategy to simplify and empower event taxonomy, collection, and analytics to better enable user behavior analysis and ease reliable data collection across multiple surfaces

• Lead all Analytics engineering across San Francisco and Austin locations. Responsible for big data analytics platforms, customer reporting, and data science. Manage 21 software engineers, DevOps engineers, data scientists, and Dev manager. Embedded on team were 3 QA engineers, 3 product managers and 1 UX designer.• Manage big data analytics platforms, analytics products, and data science. Technologies leveraged are Map/Reduce, HBase, Kafka, Spark, Elasticsearch; all on AWS.• Implemented continuous improvement Agile development practices. Transitioned development teams from Scrum to Kanban. Worked with teams to review Kanban metrics and process implementation.• Led delivery of 8 major reporting product initiatives over two year period. Worked with 4 separate product lines to schedule and deliver 8 major analytics releases. Iterated on analytics system facilitating data integration and exploration across 4 products. Created new suite of Javascript tracking integrations into tag managers (Signal, Google).• Grew one team, one location into one productive team across two locations. Doubled size of San Francisco Analytics team, from 5 to 10 in challenging hiring environment. Two promotions in four years, to leadership of all Analytics Engineering. Built local culture and made facilities decisions as engineering site lead for San Francisco• Built unified analytics tracking system from prototype to 700 million users. Included highly scalable event collection with peak processing of 75,000 events per second. Coordinated scaling and capacity planning, negotiations with out Hadoop provider, Cloudera.• Managed three major data science projects creating customer savings and new revenue. NLP system created to automatically moderate reviews creating $800k/year in savings. Rapidly iterated creation of stand-along analytics pilots creating $200k in pilot revenue. Experimented with advanced advertising look-a-like segmentation, testing pilot campaigns.

• Manage the Big Data Analytics team, responsible for creating a platform which tracks, analyzes and providers internal and external reporting on the clickstream traffic of the 500 million consumers who use Bazaarvoice's products every month. Over the past year, the team has scaled the system to handle over 1 billion events per day.• Manage the Data Science R&D team, whose focus is using predictive analytics and machine learning to produce and enhance software products. Projects include NLP and text classification of reviews to automate moderation, modeling user segments and look-alike segments for online advertising experimentation and cross-client product matching to enable network products like review syndication• Passionate about improving recruiting and on-boarding for R&D. Contributions include continued coordination of R&D orientation, leading the engineering recruiting at Stanford, co-leading the University of Waterloo co-op program and having helped to oversee the 2013 summer internship on-site activities• Managed a cloud infrastructure team whose charter was to create services and tools to help speed development and maintenance of services in a SOA architecture. Projects included an internal monitoring and alerting service built specifically to support modern AWS best practices inside a VPC

• Lead project to identify errors in all CPUs shipped by AMD, interacting weekly with 3 to 5 product teams to gather requirements, schedule, and assign tasks to successfully complete all production stages; improvements in quality and optimizations have contributed hundreds of thousands of dollars in savings to the manufacturing costs of processors• Created a cross-platform framework for developing processor diagnostics and providing functionality to enable rapid development and implementation of these diagnostics for various customers, collaborating with 2 team members • Leadership and design representation on core library development has enabled multiple teams totaling 30 developers to continue to deliver critical diagnostics for ensuring silicon quality despite a 30% reduction in resources• Represented AMD in an industry-wide standards group as part of the Distributed Management Task Force; drafted the first version of a vendor-independent specification for testing CPUs

• Analyzed Nespresso’s current North American distribution network. Designed and proposed changes and improvements to that network to handle expected rapid growth through 2015. Proposal identified a 40% cost savings to Nespresso

• Created set of major system components used in all embedded and server products created in conjunction with EFI’s 8-10 Fiery partners to prepare for integration with future document format technologies for the printing market, in order to maintain EFI’s $300M/year revenue from its Fiery business• Collaborated with product, planning, and core technology teams to help determine deficiencies in internal web application and created a replacement in Ruby On Rails• Led project design and implementation, improving time to market for product teams and improving project feature tracking system to reduce redundant development; 2 major features developed in parallel by product teams were consolidated, freeing up an engineering resource on each team to work on product requirements