I've been responsible for helping the team bringing Datadog's next-generation storage system to production. Crucial contributions to this effort include: I conceived of and implemented the "stateless" deduplication scheme at ingest--which made it possible to take on our first large internal customers. I conceived of and implemented a means to enforce data locality (similar to Snowflake micropartitions) where a best-effort schema and data ordering is established over a table at ingest and extended across all data shards at compaction. This reduced overall system cost by at least 30% by allowing early pruning of data during queries. Releasing this storage system and turning down the older one represents a 5x improvement in cost for Datadog's most data-intensive products, such as logs. I continue to explore improvements such as delayed materialization, plus additional large-scale analytical features supporting our most important products.

Tech lead for data collection, in-memory storage and checkpointing for planet-scale monitoring storage system supporting both internal systems and Cloud customers. Focus areas include scaling, reliability, architecture, multi-core, distributed systems. Championed exemplars feature for joining trace with monitoring and for analysis of long-tail data, which helps customers reduce MTTR by quickly identifying problematic servers, serving patterns, etc.

Responsible for a variety features and designs for large-scale distributed monitoring systems.

In particular: Architectural improvements to design of large-scale monitoring system to optimize high throughput push-direction collection of metric data into the monitoring system, including integration with large-scale internal customers. Historical data pipeline optimization. Advanced latency visualization. Optimized deployment of entire system to take into account network and geographical failure domains for reliability in presence of long-haul network outages including resharding system deployment and all monitoring data long these lines. Reliable, stable, performant contributions to data collection code running in every binary at Google. Performance and memory-usage optimization, with particular strength in distributed and multi-core and multithreaded systems.

Misc: technical interviewing, mentoring, oncall rotation

Responsible for development processes, from requirements gathering, through implementation and delivery, of the core computational drug design technology.

Have continued to improve the quality and speed by several orders of magnitude via algorithms, human-factors, and process improvements. This has led the drug designers to many drug design wins with external collaborators, including active drug series now in clinical testing. This has led to several innovative, active drug classes, both internally and for our partners.

Developed innovative parallel Linux cluster file system for high-bandwidth access to scientific data, enabling two orders of magnitude increase in data set size for simulation and searching.

Methodology follows a largely agile-style, with frequent releases tracking the needs of the drug design process. My strong communication skills promote very tight integration with the needs of the end users.

Developed thorough automated regression testing method which makes possible very rapid development with very few production failures, and makes it possible to deliver fundamental algorithmic improvements without compromising quality.

Developed algorithms which sped up fragment-based molecule searches by several orders of magnitude. Jointly developed method with team members for estimating whole molecule energy of joined molecular fragments that enabled significant accuracy gains in binding free energy estimates.

Worked in Multivoice VoIP gateway group and was responsible for jitter-buffering software and DSP-management software for VoIP boards. Reduced latency of VoIP calls on this product by >30%. Designed and implemented real-time cooperative (not as much of a contradiction as you might think) kernel for StrongARM processor that allowed said latency reduction by more carefully managing flows of VoIP frames into and out of DSP's in coordination with modem flows. Had key role in increasing the top capacity of the unit from 280 calls/DSP board to 672 calls/DSP board.