Denver Metropolitan Area
The most interesting initiatives I've worked on didn't start with a specific technology but rather, conversations inspired by ambitious goals. Over the past 10+ years, I've partnered closely with engineering, product, go-to-market, finance and other teams to build systems that unlock new capabilities. I'm especially passionate about unifying data across domains to inform investment and strategy decisions. Some high-impact wins from that work include: * Integrated agentic technology to cut development cycles and boost team impact. * Reduced incidents by 70% and exceeded SLA targets across terabyte-scale pipelines. * Boosted operational efficiency by 30% for a $750M business division. I'm at my best when the data team is a true business partner, not a service unit. If you're building something ambitious and want an outcome focused collaborator who starts with your goals, not just the stack, let's connect.
Deliver strategic data engineering and automation solutions for businesses, focusing on building scalable data foundations and enabling AI/ML processes and data-driven decision-making. - Contributed to AWS data lake initiative supporting AI/ML capabilities, developing foundational services to support ingestion, transformation, and management of high-volume sensitive data . - Leveraged agentic tools and IAC platforms to accelerate the development cycle and business impact. - Led the architecture and development of data pipelines, models, and end user products resulting in accelerated onboarding and staffing for a $750M healthcare business division. - Engineered a 20% reduction in Snowflake costs through strategic optimization of queries, warehouse configurations, and data modeling. - Architected and implemented a foundational analytics framework, defining key business metrics, developing pipelines and data models.
Led key initiatives to integrate new services, develop data pipelines, and design data assets to enable insights into CI/CD usage by over 2M users and 20K organizations. - Created data platform managing terabyte volumes of data to provide insight into product usage, account growth opportunities, and financial trends. - Boosted user conversion targets by 16% through the development of Customer 360 data models combining activity from CRM, marketing, and product usage data to support improved targeting and personalization campaigns. - Utilized AI & ML methods to support product usage forecasting, fraud classification, and contract data analytics. - Apply data optimizations to warehouse balancing, object storage, and query management as well as data privacy controls such as encrypting and masking. - Mentored and developed a team of analysts and junior engineers on SDLC best practices, including scripting, version control, testing, documentation, and stakeholder management, fostering a culture of data excellence. - Administered Snowflake, dbt core, and Looker project environments, including pull requests, package maintenance, and ci/cd jobs while ensuring proper data governance and security.
Partnered with core business groups and leadership to define metrics and develop data assets to support aligned decisions across multiple brands and markets exceeding $130M. - Supported web/app product feature testing and GTM campaign analyses evaluating conversion, investment, and retention performance for over 1.3M monthly users. - Increased DAU by over 2000% in the first month post launch of a new messaging product through the development of data pipelines, data models, and reporting assets. - Applied forecasting, regression, clustering, decision trees, and other statistical methods in analyses and visualizations to support sound decision making. - Supported and interpreted A/B split testing campaigns for product and marketing campaigns. - Diagnosed low performing funnel stages to identify issues generated from user actions and application behaviors, enabling teams to quickly respond to achieve conversion targets. - Contributed to migration to Redshift through the development of data pipelines and data models. Utilized AWS services including Kinesis, s3, Lambda, Glue, and EC2. - Developed data models using SQL and python, utilizing Kimball, Star, and Snowflake design patterns to ensure scalability and flexibility. - Managed dbt Core, Redshift, and Tableau environments. Implemented data product development standards and code review process.
Modernized digital infrastructure for the Capital Finance Division. - Defined capabilities for the development of new data lake, warehouse, and reporting assets, producing data map artifacts, data models, transformation scripts, and QA tests. - Developed analytical scripts, using SQL and python, to surface KPIs as well as dynamic dashboards to support proactive monitoring.