Amazon from 2021 to 2023, Coachable since May 2023. The one-page resume has the same facts.
Coachable
Software Engineer
May 2023 to now
New York, NY
Coachable is an education startup that helps engineers land roles at top tech companies through software-driven career prep and coaching.
95%
placement rate
300%+
year-over-year user growth
3x
faster reporting
50%
better coach-to-student efficiency
Engineered and scaled the company’s coaching platform and data systems, supporting 80+ active learners and sustaining a 95% placement rate into roles at Google, Amazon, Meta, Microsoft, and startups.
Designed analytics pipelines to track student performance, hiring outcomes, and curriculum insights; enabled 3x faster reporting and improved data accuracy by 40%.
Built and maintained internal tooling in Python, React, and AWS, including LLM-poweredresume-generation and delivery pipelines, improving coach-to-student efficiency by 50%.
Coached 200+ students in data structures, algorithms, and problem-solving fundamentals; guided students through 1,000+ mock interviews resulting in hundreds of offers.
Collaborated with the founding team to scale operations, contributing to 300%+year-over-year user growth.
Amazon
Software Development Engineer, Alexa Data Depot Team
July 2021 to January 2023
Cambridge, MA
The Alexa Data Depot team manages high-volume, privacy-sensitive data pipelines powering Alexa customer experiences and analytics.
700M+
objects in deletion and retention workflows
99.9%
SLA on 300+ on-call tickets
40%
faster pipeline recovery
0
privacy violations
Led data deletion and retention workflows handling 700M+ objects (>55 PB of data) across distributed S3 buckets, achieving $1.3M in quarterly cost savings through optimized lifecycle policies and automation in Python and Java.
Served as Security Lead for the team, identifying and resolving 80+ security risks (including 25+high-risk and 2 critical, org-wide escalations) through IAM hardening, encryption enforcement, and privacy compliance.
Owned and maintained the data deletion job ensuring GDPR and CCPA compliance across petabyte-scale datasets, collaborating with privacy and legal teams to ensure zero violations.
Owned the monthly on-call rotation, resolving 300+ tickets at 99.9% SLA.
Partnered with cross-functional teams to enhance data observability and improve pipeline recovery times by 40%.
Education
Harvard University
A.B. Computer Science
May 2021
Secondary in Ethnicity, Migration and Rights
Spanish Citation
Honors
Forbes 30 Under 30 Scholar ·HSF Scholar ·Tuskegee Airmen Scholar ·NSA Scholar ·Dominican Day Parade Scholar
Research
Automated CS Grading System, LLVM Research
University of Illinois Urbana-Champaign
Developed an automated grading and hint-generation system for large CS courses using LLVM, C/C++, and Python, improving grading throughput by 10x.
One of ~40 students nationally selected for the DREU Research Program, collaborating directly with graduate students and faculty.
Six projects I built and run on my own. Each one is live on the web or on the App Store, and each has a case study.
Backend systems
Live ledgerline.adelsonaguasvivas.workers.dev
Ledgerline
An HTTP API on Cloudflare Workers for append-only event streams: exactly-once appends, gap-free order, and a SHA-256 hash chain that makes rewriting history detectable.
Fig. 1The path of one append
Client
WorkerHono
Durable Objectone per stream
D1read model
Client to Worker 01POST /v1/streams/:id/events with an Idempotency-Key
Worker 02Key, rate and owner checks The API key is looked up by its SHA-256 hash, its token bucket spends one token (429 with Retry-After when empty), and the stream must belong to the key.
Worker to Durable Object 03append(payload, key) routed by idFromName(streamId)
Durable Object 04blockConcurrencyWhile A known key with the same body returns the original {seq, hash}; a different body is a conflict. Otherwise seq + 1 and the next hash.
Durable Object 05One atomic write The event, the key record, the minute rollup, the stream head and the outbox alarm. All or nothing.
Durable Object to Worker 06{seq, hash}
Worker to D1 07INSERT OR IGNORE The fast path, so a read right after a write usually sees it. If it fails, the client still gets its answer.
Worker to Client 08201 {seq, hash} A replay gets 200 with Idempotent-Replay: true; a different body gets 422
Durable Object to D1 09alarm(): outbox sweep About 5 s later it inserts only the rows D1 is missing, and retries every 30 s while D1 fails.
Hash chain
genesisSHA-256("ledgerline:v2:" + streamId)
h1event 1
h2event 2
h3event 3
hn = SHA-256(hn-1 | RFC 8785 canonical JSON | n)
/verify recomputes the chain from genesis, checking every seq, prevHash and hash, then compares the result with the committed head, so a deleted tail fails too.
One TypeScript rules engine, about 1,240 lines with zero runtime dependencies, runs on the server and in every client: the web, the iPhone app and iMessage.
The server re-runs every move through the engine and rejects a stale write with HTTP 409.
A quiet table refetches its state every 10 seconds, so a missed realtime update cannot freeze a game.
Ledgerline422 for a reused Idempotency-Key, RFC 8785 hashing as chain version 2, an outbox for the D1 read model, and a new page with the tamper demo up front.
An iPhone app for Hype Room, a sneaker and streetwear store: a free weekly draw, the store catalog, and in-store holds. Pre-launch demo with sample data.
Each weekly draw has a dated id, and a test sweeps every hour of three weeks, including both DST changes, to check that two draws are never open at once.
Hold codes skip 0, O, 1, I and L so staff can read them at the counter, and holds expire after 48 hours.
I studied computer science at Harvard, then spent a year and a half at Amazon building data deletion and retention systems for Alexa across more than 55 petabytes of data. Today I’m at Coachable, an education startup, where I build the coaching platform, the analytics pipelines and internal tools.
I’m Dominican and grew up between Spanish and English. That shapes how I build: bilingual interfaces by default, and projects with a piece of home in them, from dominoes and baseball to the Malecón and the conuco.
I build outside work because I like following an idea through the whole product cycle, from the engine and the data model to the screens people use. Recent releases include two iPhone apps and several browser games.
