Lyle Poisson

About

Experienced senior data engineer offering expertise in enterprise data solutions, cloud data engineering, and high-level strategic planning. Proven track-record leading technical teams to develop, analyze, and scale diverse, big data assets and pipelines aligned to organizational objectives with a focus on data modeling, scalable workflows, and optimization. Deep technical expertise combined with an understanding of highly effective data pipeline design and management, technical leadership, and stakeholder management—collaborative in building cross-functional teams and consistently exceeding performance metrics.

Experience

• Senior Data Engineer · Proxify and Independent Consulting2025

   Designed, built, and maintained serverless pipelines and Streamlit interfaces to process ~500GB of multi-domain data (financial, industrial, geographic, geopolitical), directly supporting analyst and stakeholder decisions at Trafigura.  Partnered with CEO at a pre-seed startup to architect and deliver a greenfield financial data pipeline, ingesting 20 years of SEC filing data across 4,000+ companies into clean, LLM-queryable financial statements.

• Lead Data Engineer · DataPraxis2024 – 2025

   Developed a scalable, stable, and automated solution to a pre-existing manual process for a nascent, ad hoc survey data analytics service, saving over 6 hours per survey ingestion.  Built a self-hosted container-based ingestion and analytics platform from scratch, using Google Compute Engine, Docker/Kubernetes, and Windmill workflow engine, reducing ingest time from 45 to 3 minutes.  Developed and enforced team-wide engineering best practices and developed automated tests and toolsets to allow the entire Analytics team to write consistent, bug-free code.  Leveraged AI tools such as ChatGPT and Claude Code to improve prototyping speed by 70%.

• Data Engineer · Municipal Securities Rulemaking Board (MSRB)2022 – 2024

   Oversaw data engineering for an organization with an operating budget of over $47MM annually, creating 30 ETL pipelines to improve transparency in the $4 trillion municipal bonds market, facilitating over $9B in trades per day.  Spearheaded a $50K pipeline upgrade project as the sole engineer, increasing pricing yield curve data availability on 2.7MM securities by 800%, allowing live real-time data for investors with over 13MM trades in 2023 globally.  Served as lead subject matter expert in pricing and securities data on a team of 15, driving a 70% improvement in team performance in these areas, while delivering an average of 20 new features per bimonthly release.  Reduced full data load time from 3 weeks to 6 hours, cutting operating expenses by 95%, saving over $50K per year, while developing and maintaining data curation and publishing pipelines that maintained >99% uptime in production.  Automated table DDL comparison, reducing operating time from 30 hours to less than 3, saving the data team $7K in labor costs per year, while mentoring 3 data analysts in optimizing SQL queries, improving runtime by 500%.

• Data Engineer · Catalist LLC2019 – 2022

   Led a team of 5 in a full restructure and overhaul of a suite of 9 analytics models, improving performance by 60-70% with model pipelines remaining resilient for >3 years, saving >$200k and months in development labor and time costs.  Developed a wrapper script to test data by state instead of nationally, reducing the database load and increasing test speeds by 500x, while reducing the number of full national loads from up to 10 per model to 1, saving $5K per month.  Productionized machine learning and analytics models to run on a scale of 1,000,000x the development dataset while maintaining operational efficiency and accuracy, while mentoring a new hire, improving performance by 60%.  Served as subject matter expert on infrastructure, data, processes, and analytics models for a group of >20, while enhancing a phone number matching model by 4x for 85MM records, increasing client outreach capabilities by 100x.

• Data Science Fellow · MATClinics2019 – 2019

   Independently developed and executed a machine learning study of over 50 columns of patient intake data, identifying 3 key metrics and creating an algorithm to improve patient intake process compliance by 30%.  Streamlined and refactored the patient intake process, multiplying the data usability by 20 and allowing management and case workers to access consistent and detailed data, saving over $800 in labor each month.  Created and implemented a business dashboard that allowed the company to save over $5K in operating expenses, by improving operating efficiency, reducing scheduling conflicts, and improving outreach and SEO efforts.

Education

• Johns Hopkins University, Whiting School of EngineeringMaster of Science, Applied Mathematics & Statistics
• Johns Hopkins UniversityBachelor of Science in Applied Mathematics & Bachelor of Arts in Mathematics, Applied Mathematics; Mathematics

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