Medialab is a holding company which owns a number of category-leading media properties including Genius (lyrics), Imgur (memes), and Worldstar HipHop. I lead a team that touches all of the properties and oversees the centralization of data assets throughout the company. It's early days yet, but some exciting achievements so far:* Classifying Imgur content using AI, producing extensive sets of classifiers such as category, emotion, description, and tags for previously-unlabeled user-generated content* Leading the rollout of Mixpanel across all Medialab properties* Introducing many best practices to the data team at Medialab, including data quality checks in ETL processes; use of slowly-changing dimension tables; and semi-persistent staging tables to aid in debugging* Establishing a single source of truth for revenue reporting

I led a team of data engineers and data scientists, as well as developing our nascent business intelligence function. Achievements included:* Introduced Airflow as the primary orchestration layer for code execution, along with numerous best practices such as pre-commit hooks and fully documented code; all of which have been adopted throughout the engineering organization* Advocated for and took the lead on introducing operational improvements in the engineering org including adopting agile methodology/scrum; developing and adopting a career skills matrix for engineers; and implementing a dedicated dev ops/engineering enablement team* Rebuilt major portions of our core pipelines, moving from iterative scripts running in Spark to highly performant SQL jobs running on Snowflake and Redshift* Coached engineers with no previous experience in core data engineering technologies on how to realign their thinking from python-based iterative data manipulation to SQL-based concurrent transformations* Led the adoption of AWS technologies as our default platform for new applications* Developed a performant caching architecture to support our primary customer-facing applications* Continually supported the data science team in the development of Media Mix Modeling, introducing a new product line and monetization source* Supported the data science team in the development of a generalized framework for building machine-learning models

I led a team of outstanding engineers and analysts building world-class products that democratized access to information across a rapidly-growing, data-hungry organization. My role was split between providing vision to the team - guiding our roadmap and determining what products we should develop - and going heads-down writing my share of code (Python and SQL, with occasional dives into the Ruby on Rails and JS apps).Some highlights of my time at Teachable:* Built a Single Source of Truth data warehouse (on Amazon Redshift) and exposed business-ready fact tables with cleaned and corrected data* Developed a company-wide BI solution, integrating the warehouse with Looker and Sigma for end-user analytics; a comprehensive data dictionary and data management tool; and real-time monitoring of data quality with anomaly-based alerting* Built a kafka-based event/pixel pipeline (replacing an unsatisfactory experience using a 3rd party) using asyncio and other technologies; processed millions of daily events, with outputs going to both the data warehouse and external integrations (Mixpanel, Iterable, promoter.io)* Implemented Apache Airflow running on EKS, pioneering the deployment of applications on Kubernetes at Teachable* Introduced agile methodologies (scrum for engineering and kanban for business intelligence) and championed them within the engineering org* As a member of the technology leadership team, continually advocated for improved diversity in our hiring process* Developed team members from junior and mid-level engineers and analysts to senior ICs and managers, coaching multiple people as they took on their first managerial roles

Freestar is a monetization solutions company serving digital publishers by providing an array of technologies including ad tech, header bidding, and machine learning products. Inc 500 #1 fastest growing company in 2019. I led a small agile team of engineers/data scientists in a microservices environment where we worked daily in java, python and javascript. * As a member of the executive team, I was a voice in all of our strategic planning and roadmapping. I worked closely with other business leaders throughout the company to set goals, determine appropriate KPIs, and prioritize projects.* I was one of three senior engineering hires who joined the company at the same time, each with a different specialization (infrastructure, product, and data). Together we built a world-class microservices-based application from the ground up.* Hired and developed the data team (engineers and data scientists). Managed the agile development process and implemented standard scrum rituals.* Built and maintained a real-time streaming pipeline, which processed and stored hundreds of millions of daily events using Apache Beam and Google Pub/Sub.* Architected and built the data warehouse using BigQuery.* Using the Java SpringBoot framework, built various services and APIs, serving up the data that powered our reporting frontend.* Built real-time reporting, giving our customers views into the performance of their digital marketing campaigns as they happened throughout the day.* Provided guidance and oversight to our data science team, who built products using a variety of ML and NLP models

At Hearst I managed an engineering team that built the data sources underlying products used throughout the company. I regularly wrote ETL scripts gathering Big Data (terrabytes/day) from varied internal and external sources, such as Google DFP, DBM, DCM/DFA, GA; Appnexus; Facebook; Adobe Omniture, etc. We delivered data via APIs to front-end development teams, and reports and dashboards to business users and owned the data pipelines that process hundreds of millions of webpage hits daily, producing real-time page stats and revenue projections.

Built reporting infrastructure using MySQL and Tableau Server. Implemented Amazon Redshift as data warehouse platform. Provide data and analysis to support finance, marketing, business development, and management; develop customer segmentation models; provide data integrity; develop on-demand reporting and dashboards via Tableau over Redshift backbone; provide financial reports to internal and external clients. Expert-level SQL developer with hands-on experience developing big-data warehouse in a rapidly-growing internet startup in the digital media space. Develop ETL tools in Python/BASH/SQL. Company acquired by Amazon, April 2014.

Provided periodic and ad-hoc statistical reports and analysis to Deans, directors, faculty and staff; examined and analyzed admissions and registration records, made recommendations on efficiencies; built and supported SQL Server database; built and distributed Hyperion Brio and OBIEE reports; built and deployed customer surveys and provide post-survey reports and analysis; performed ETL on data from myriad sources; provided end-user reports and visualizations using Tableau BI platform.

Built relationships with publishers and content creators; developed columns and podcast, hired writers, edited online content; performed MySQL database maintenance and administration; developed financial plans and projections; tracked cash position, budget, expenditures, and revenues; worked closely with CEO and CTO on development of short and long-term strategies and implementation plans.