Lead a 15 member cross-functional team in building requirements, design, and implementation of an LLM-driven AI Data Agent. Enable users across the company to subscribe to metric values & alerts, discuss metric definitions and trends, and discover new metrics & data products with an AI Slack agent.Lead architecture, design & implementation of a Metrics Store to serve as the new interface for certified metric definitions and metadata.

Led the data & application engineering efforts of a 7 member team developing a Slack-based, self-serve analytics application in Python which delivers trusted product usage data to success managers & customers. This automation replaced a manual process for business analysts, saving 1,500 engineering & analyst hours while influencing retention & expansion for accounts totaling ~$1B in AOV.Led team of 4 engineers developing a Slack Data Assistant which automates common technical data engineering requests, such as new data pipeline creation. Application integrates with our Snowflake data warehouse, Github & Jira environments, and a combination of code templates and AI to generate code, create pull requests, create & share new data objects, and more. Assistant has processed over 2,000 requests in its first year of production use, saving hundreds of engineering hours while reducing request processing time by ~80%.

Designed and implemented foundational business data models for Slack's product & feature usage metrics in our Hive/Hadoop data warehouse. Populated these metrics via SQL pipelines orchestrated in Airflow. Delivered refined versions to Slack's Snowflake data warehouse to support Business Intelligence and Data Science initiatives.

Designed & built automated daily key financial metrics forecast in Python using Prophet, orchestrated with Airflow and hosted on AWS. Created and maintained data pipelines and foundational datasets in new Snowflake data warehouse for internally & externally reported financial, sales, and product metrics. Supported visualization of daily, weekly, monthly, and quarterly reports in Tableau. Backed ad hoc reporting & analysis for VP, C-level, and Board-level audiences.

Built and tuned training data sets for newly developed machine learning algorithms and neural networks. Engineered and maintained automated, end-to-end data pipelines in Python and R for data collection, storage, visualization & reporting. Utilized natural language processing techniques to classify documents based on content, extracting key pieces of information to store in a normalized SQL database. Scraped web sites and called APIs to collect new data from traditional and alternative sources.

Assisted lead analysts preparing credit committee reports, including in-depth financial and qualitative research on companies and industries in Latin America. Performed corporate financial statement analysis, developed and maintained financial models, collected and tracked key industry statistics, and actively participated in credit rating committees to support rating decisions.

Analyzed fund portfolios, new fund business prospects, and fund-related business processes for client portfolios.

Prepared investment presentation used to successfully raise equity financing for a privately held Chicago company. Analysis included precedent transactions, market research, and data analysis.

Performed data research and analysis for the Morningstar Indexes, Equity Research, and Fund Research teams for use in international research reports and client requests.