Tyler Reagan

▸Underwent a deliberate, full-stack transition — dedicating months to React, Next.js, TypeScript, Electron, and Node.js — becoming a capable frontend engineer after years as a backend/infrastructure specialist.

▸Sole engineer across frontend, backend, and infrastructure for CytoCanvas, a spatial cytoprofiling visualization platform enabling researchers to interactively explore Flow Cell, Montage, and Cell Table views over large scientific imaging datasets.

▸Deepened Go expertise through advanced concurrency work: implemented a Temporal workflow for S3 Glacier archive restores using heartbeat-based long-polling to safely orchestrate multi-hour operations without activity timeouts.

▸Designed gRPC service contracts from scratch (DesignsService, ArtifactsService) — including proto schema, error model, artifact rollback, and a pre-validate RPC pattern — gaining strong experience in microservice API design.

▸Drove major analytics performance improvements: migrated the Cell Table to AgGrid SSRM + DuckDB-wasm, cutting per-query effect time from 90ms to 40ms; achieved 50% time-to-first-render reduction on the Desktop app through zarr preloading and parallelized tile fetches.

▸Heavily leveraged agentic development tools (Claude, Cursor, prompt engineering, skills/planning patterns) to accelerate delivery as a sole engineer across a broad and evolving scope.

▸Introduced, developed, and maintained Apache Airflow (AWS MWAA) as the orchestration backbone of the data platform — going from zero to owning the full DAG ecosystem for clinical research dataset generation pipelines.

▸Became proficient in Go through ownership of the team's ETL platform, with particular depth in pipeline optimization and Go memory management — reducing EKS pod memory usage by up to 30% by offloading in-memory work to AWS managed services.

▸Led first real Terraform ownership: reduced the codebase's Terraform file count by 94% (520 → 29) under a new infrastructure model combining Go binaries (Bazel), Airflow DAGs (Python), and Kubernetes pod operators (EKS).

▸Designed Docker-based test environments and S3 branch-state synchronizations that reduced integration testing turnaround from days to hours, directly improving developer velocity.

▸Built and maintained Go-based ad metrics ETL pipelines processing revenue and engagement data for thousands of publisher sites, gaining strong exposure to the scale and reliability demands of adtech data infrastructure.

▸Developed hands-on AWS fluency across Kinesis (real-time ingestion), Redshift (analytics warehouse), S3, and SNS — working directly with the services that underpin high-throughput data pipelines.

▸First professional introduction to data pipelines: built an Apache Airflow workflow in Python to automate PHI scrubbing of MySQL databases via Bash and SQL scripts, deployed on AWS with Docker.