Software Engineer

LYXA is a multi-service delivery platform. I work across the backend as an owner of core infrastructure — identifying systemic problems before they're assigned to me, driving the solution end to end, and shipping across a distributed system under continuous production load. I'm the go-to on async architecture, real-time infrastructure, and query design — teammates come to me when they're stuck on something hard. I contribute to code reviews on high-stakes changes and have helped teammates reason through tradeoffs they hadn't encountered before. Several of the patterns and libraries I introduced have been adopted as team standards across all services. Every piece of work here has touched production traffic.

Event-Driven Infrastructure

I identified that async work across the platform was being done synchronously — services calling each other directly to trigger downstream actions. This created tight coupling: a failure in one service could cascade to its caller, duplicates reached end users, and failed deliveries were dropped with no retry and no trace. The pattern was repeated independently across 16 services with no shared standard.

I proposed migrating to a fully event-driven model and designed the architecture end to end: event contracts, queue topology, retry strategy, and idempotency guarantees. I chose a message broker over alternatives like DB-backed queues because it gave us durable delivery, backpressure handling, and horizontally scalable consumers without coupling producers to consumer availability. Producers emit events; a stateless consumer handles delivery with exponential backoff and exactly-once guarantees. The old and new paths ran in parallel through the phased cutover across 30+ endpoints — I coordinated the rollout with the rest of the team, reviewed every cutover PR, and called the go/no-go on each service. Zero downtime. Zero regressions. Delivery incidents dropped to zero. The event contract and queue design I introduced became the team's standard for all subsequent async work.

The same fanout pattern solved a second problem: bulk operations blocking the request path under high data volume. I proposed replacing synchronous bulk writes with an async fanout — input paginated, all page events published in parallel, each page an independent idempotent unit that retries without affecting others. An operation that previously timed out entirely returns near-instantly regardless of input size.

The underlying messaging library was also migrated from raw channel management to an auto-recovering connection layer. Services now reconnect to the broker automatically after disruptions — no restarts, no on-call intervention required.

High-Frequency Event Streaming

The platform processes thousands of real-time updates per minute from a large pool of concurrent producers. I identified that the real-time delivery and persistence concerns were unnecessarily coupled — every incoming event was competing with request-path work and creating direct database write pressure, which meant live delivery latency was tied to how fast the database could keep up.

I designed a dual-path ingestion pipeline to decouple them. I chose this over a single async path because the real-time consumer needed sub-millisecond delivery while the persistence consumer needed correctness guarantees — they have different requirements and combining them would have forced a compromise on both. Each incoming event takes two concurrent paths: an immediate broadcast to all active consumers, and an enqueue to an async layer that deduplicates by latest value per producer before persisting via batched bulk writes. Real-time delivery is fully decoupled from write time. Write volume is materially reduced by collapsing redundant events before they reach the database. The pipeline handles 1,000+ concurrent producers with lower overhead and better delivery consistency than per-event direct writes.

Real-Time Event Delivery

Real-time features across the platform — messaging, live status updates, action events — all needed the same thing: an event published by any server instance reaching exactly the right connected client instantly. A single-instance pub/sub breaks under horizontal scaling. Broadcasting to all instances wastes processing and doesn't scale with connection count.

I drove the design and implementation of a shared real-time event delivery library, starting from the WebSocket gateway service itself: connection lifecycle, disconnect cleanup, and resolving infrastructure configuration issues that were preventing the service from binding in the target environment. That foundation became the base every subsequent real-time feature was built on.

On top of it, I built a two-tier delivery library. I chose Redis per-instance queues over a broadcast model because it gives exact delivery with zero wasted processing — only the instance holding the target socket receives the message. Redis handles cross-instance routing; an in-process registry handles the final hop with zero-copy local emission. Presence is tracked using scored sets so online status and connection counts resolve in a single query. A local LRU cache eliminates redundant Redis reads on hot delivery paths. Clients with no active connection fall through to push notifications automatically. Instance crashes clean up via Redis without intervention.

I documented the library's interface and integration contract when I shipped it so teams could wire up new event types without coming to me. The library grew without my direct involvement after the initial release — teams add new event types by registering against the existing infrastructure with no routing, presence, or fallback logic to reimplement.

Observability

Before this work, debugging a production incident meant hours of grepping through inconsistent log output across services with no shared schema and no way to correlate a request across service boundaries. Many failures were unresolvable because the system state at the time of failure was gone by the time anyone investigated. Engineers were guessing and often failing to reproduce.

I proposed a platform-wide structured logging standard and designed and shipped it as a shared library through the core package. I chose MongoDB-persisted structured records over an external logging service to keep the system co-located with the data it describes, avoid an additional vendor dependency, and make log records queryable with the same tooling the team already uses. Every service emits typed, indexed records into a single collection using the same schema. Request and caller context propagate automatically through the full async call tree — no service needs to thread identifiers manually. Retention is self-managing.

With the library in place I instrumented every critical execution path across the platform. On high-stakes request paths, each operation produces a single lifecycle record — captured at entry, then updated with success metadata or a fully normalized error shape on completion. On multi-step async paths, each stage emits a discrete tagged state. Nothing is lost on the first occurrence.

The library was adopted across all services with zero per-service configuration required. Engineers now start from structured, queryable evidence on every incident. What used to take hours — and often ended without resolution — now takes minutes. The shift also changed how the team approaches instrumentation: engineers instrument new paths without being asked because the value is visible and the tooling is already there.

Query Performance

I identified that slow query patterns were not isolated bugs but a systemic problem distributed across multiple services: unbounded collection scans, oversized aggregation pipelines, N+1 chains, and overfetching. Under concurrent load these patterns regularly saturated database CPU and caused cascading slowdowns. Queries on critical paths were taking 4–8 seconds. Some timed out entirely.

I proposed a phased optimization program rather than a rewrite — a rewrite would have taken months and shipped nothing in the interim. I chose the phased approach because each fix could go live independently, deliver measurable value immediately, and be validated in isolation without waiting for a full replacement. Across 30+ high-traffic surfaces I audited real execution paths, identified whether the bottleneck was a missing index, a pipeline structure problem, or a fan-out pattern, and shipped a targeted fix: collection scans replaced with compound indexed fetches, N+1 chains collapsed into batched lookups, aggregation pipelines restructured to push selectivity earlier, projections scoped to what callers actually consume.

One of the highest-impact fixes was the platform's most frequent write path — a per-item validation loop that fired on every cart update. Each item triggered independent queries proportional to cart size. I replaced the serial fan-out with a batched bulk fetch and parallelized the validation stage. The operation now runs in constant time regardless of how many items are in the request.

Queries that previously took 4–8 seconds now execute in under 150ms on the same data. Database CPU spikes disappeared. The platform held stable at peak traffic where it previously fell over. Before turning it into a standard, I ran a session with the team walking through the patterns I'd found and the fixes — the goal was making sure engineers understood why, not just what to avoid. I established a query review standard from this work that the team now applies when adding new endpoints — the same class of problems has not recurred on any instrumented path.

Lifecycle Automation

I identified a class of problem that kept surfacing across the platform: entities with a full time-bounded lifecycle — creation with prerequisites, scheduled state transitions, live mutations with safety constraints, and cleanup on termination — being managed manually or through ad-hoc tooling. Naive implementations were leaving orphaned state, permitting invalid records to go live, and had no safe path for modifying in-flight entries.

Before building, I ran a design review with the team to walk through the edge cases — the confirmation gate and the singleton constraint both came out of that conversation. I designed and built a fully automated lifecycle management system. A prerequisite gate blocks creation unless required configuration is present. A singleton constraint enforced at write time prevents concurrent creation requests from both succeeding. I chose a job scheduler with deterministic job IDs over cron polling because deterministic IDs make rescheduling safe — you can cancel and replace a job by ID without risking duplicate or orphaned timers. State transitions fire at the exact configured time with no polling overhead. Termination removes all associated records atomically. Mutations to in-flight entries require an explicit confirmation gate. Rescheduling always cancels existing jobs before queuing replacements.

The full lifecycle runs without engineering involvement at any stage: creation, scheduling, activation, editing, and cleanup are all handled by the system.

Assignment & Scoring Engine

The existing assignment flow lacked a scoring model — candidates were selected without any objective signal for fit, performance history, or eligibility weighting, and the retry logic was primitive. I proposed and built a replacement engine from scratch.

I designed a multi-factor pipeline: proximity and eligibility filtering, fit scoring, parallel offer broadcasting, and full lifecycle management across acceptance, rejection, timeout, and expiry. For retry I chose a multi-cycle expanding search model over a single-pass selection because a single pass has a hard cutoff — if no candidate accepts, the job fails. Expanding search keeps trying across progressively wider ranges while the configurable batch sizing prevents overwhelming the pool. Simultaneous acceptances are resolved atomically — one candidate wins, all competing offers cancelled in a single write.

Alongside the engine, I built a scoring system that continuously evaluates agents across behavioral dimensions using event-driven updates. I worked directly with the operations team to define which dimensions actually predict good performance — they had the domain knowledge, I built the system that could measure and act on it. Scores derive from accumulated operational data and feed directly into every assignment decision. New agents get a fair baseline. Experienced agents are ranked by actual behavior, not seniority. The scoring data is now used by operations teams to make performance decisions without manual review.

Integration & Sync

The business needed to sync large, frequently changing catalogs from a third-party provider — stock levels, prices, and availability — across all shops. A sequential sync was too slow for catalogs of this size, prone to mid-run failure, and would leave stale data visible for long windows.

I designed the sync architecture and chose an event-driven fanout over a sequential worker because a sequential approach creates a single point of failure — one bad page stops everything, and total sync time grows linearly with catalog size. With a fanout, every page is independent: a failure affects only that page, and all pages start immediately rather than waiting for the previous one to finish. The producer probes total catalog size, divides into pages, and publishes all page events in parallel. Each consumer fetches its page from the provider, runs a deduplication pass before touching the database, and persists via a single batched bulk write. A config-driven control plane makes page size, sync interval, time window, and the active toggle tunable at runtime — non-engineers can adjust sync behavior without a deployment.

Catalogs of 7,000+ items complete as independent parallel jobs — sync time does not grow linearly with catalog size. A failing page retries without affecting the rest. Every job logs its outcome so any discrepancy is immediately traceable.

File Storage

File storage across the platform was tightly coupled to a single cloud provider — upload calls, path construction, and access logic were all vendor-specific and scattered through business logic. Any provider change would have required touching every integration point across multiple services.

I designed and shipped a provider-agnostic storage library and proposed distributing it through the shared core package. I chose a factory pattern with two distinct access models over a unified interface because the access semantics are fundamentally different: public files are served through a CDN with long-lived cache headers so the provider has no involvement after the initial upload; private files require signed URLs with short TTLs so callers never handle credentials directly. CRC32C integrity validation runs on every write — corruption is caught before the record is persisted. The factory selects the backing provider from configuration, so swapping providers is a config change with no code changes required in any service.

The library is now adopted as the standard file storage abstraction across the platform. Teams integrate against a single interface with no knowledge of the underlying provider.

Routing & Distance

A core platform computation was being handled by a third-party API on every request — per-request cost compounding at volume, external latency on a sensitive path, and a hard external dependency on something the platform could not afford to have fail.

I proposed replacing it with a self-hosted service. I chose self-hosting because the per-request cost model does not scale and the external dependency created a failure point we had no control over. The service runs on Kubernetes with a warm initialization path — the heavy computation loads once at startup and stays resident, so there is no cold-path overhead per call. Internal callers hit the self-hosted endpoint with no interface changes.

Per-request third-party API cost was eliminated entirely. The computation is now fully within our infrastructure with no external SLA dependency.

Platform Middleware

Operational concerns that apply to every API procedure — authentication, performance monitoring, and session management — were handled ad hoc per route. There was no enforcement mechanism, no guarantee of consistency, and adding a new concern meant touching every handler individually.

I designed and built a composable middleware stack applied globally across the API layer. I chose a composable factory model over a single monolithic middleware because individual concerns need to be combined independently — not every procedure uses the same auth variant, and adding new combinations should not require new middleware code. Each layer is self-contained and can be composed without modifying the others.

A slow-API interceptor wraps every procedure and automatically emits a structured log entry when execution exceeds the latency threshold — full call context is captured on the first occurrence with no per-procedure instrumentation. The auth layer is a factory that produces middleware for different entity types; adding a new entity to the auth model is a factory entry, not a modification to existing logic. Token renewal is handled transparently in-flight — clients on a valid session never receive an expired-token error. CORS is configured with a 24-hour preflight cache to eliminate redundant OPTIONS round-trips on every cross-origin call.

The stack is applied globally. Every procedure gets performance monitoring, correct auth enforcement, and session continuity with zero per-route setup.

Service Consolidation

I identified that the platform had accumulated several standalone deployable services handling closely related domains — each carrying its own Kubernetes deployment, autoscaling config, ingress rules, CI/CD pipeline, secrets, and environment config. Every cross-domain change required coordinating releases across multiple services, and on-call coverage had to span all of them independently. I proposed consolidating them and made the case based on operational cost, deployment complexity, and incident surface area. Before starting, I presented the migration plan to the team and walked through what would change for each domain — alignment upfront meant no surprises mid-migration.

All modules were migrated and wired into a consolidated router. Kubernetes ingress was updated in both production and preprod. Multiple independent deployments — along with their autoscalers, pipelines, and environment configs — were eliminated. The migration required a deliberate revert-and-re-land cycle when a regression surfaced mid-way: rather than pushing through, I reverted cleanly, resolved the issue in isolation, and re-landed. That discipline kept production stable throughout.

Infrastructure cost dropped by removing the compute overhead of running separate deployable units. CI/CD pipelines were cut, reducing the places a misconfiguration can cause a failed release. Cross-domain changes that required coordinating deploys across multiple services now ship in a single merge. On-call scope shrank — fewer independent failure domains to monitor, fewer places an incident can originate.