Staff Software Engineer – Data Platform & Backend Infrastructure

I am a staff-level software engineer, hands-on technical lead, and engineering manager focused on performance-sensitive data platforms, backend data systems, and large-scale analytical processing. I design Snowflake and Spark/EMR systems over 10T+ record datasets and build Scala/Java extensions, UDFs, lookup jobs, backfills, and transformation code.

I specialize in turning cost- or timeout-constrained analyses into reliable production runs. My work centers on query optimization, workload optimization, data modeling, production escalation, and technical leadership across high-volume geospatial signal systems.

At Unacast, I was promoted from Senior Software Engineer to Manager, Software Engineering for the Complex Algorithm Team. I led work on high-scale geospatial data systems, analytical performance, and production data reliability while serving as a technical escalation point for roughly 1,000 daily production data jobs.

I redesigned Snowflake data models, query shapes, and execution strategies to avoid cost, timeout, and optimizer bottlenecks on datasets exceeding 10T records. I also developed Scala/Java JARs, UDFs, and production data-processing code for Snowflake, Spark/EMR, S3, and DynamoDB systems.

My background also includes backend and distributed systems work at Leidos, where I rewrote C++ REST API code paths and backend analysis as Scala services while preserving interoperability with legacy Fortran systems. Earlier, at Xometry and NIST, I worked on computational geometry, machine-learning feature generation, scientific software infrastructure, and automation for research and acquisition decisions.

I hold advanced training in mathematics, including a PhD in Mathematics by the end of this year from West Virginia University, along with an M.S. and B.S. in Mathematics and Physics. My research and publication history includes performance optimization for gravitational-wave analysis and other mathematical work.