Led modernization of the transaction platform supporting 3,000+ kiosks, processing ~$30M/month across multiple cryptocurrencies, while meeting compliance and security requirements in a regulated fintech environment

Migrated from a licensed transaction backend to in-house, event-driven services, reducing per-transaction fees by 15% and improving platform control, extensibility, and vendor independence

Architected and shipped distributed transaction services on GCP using Node.js and containerized microservices, enabling consistent APIs and secure processing across kiosk, mobile, and in-store channels

Built a production-grade delivery pipeline with Terraform (IaC), automated CI/CD, and blue/green deployments, improving release cadence bi-weekly → intra-weekly and reducing production incidents by 40% through safer rollouts and standardized controls

Provided senior technical and product leadership, guiding cross-functional engineering teams and collaborating with executives to align technology strategy with business priorities. Established engineering quality gates (secure coding standards, required reviews, automated tests, CI checks), reducing regression risk and improving deployment confidence for mission-critical financial workflows

Introduced AI-assisted development workflows (templates, tooling, review automation), improving team throughput by ~25% and increasing consistency across service patterns and code quality expectations

Provided senior leadership across engineering and product teams, aligning software delivery with business strategy and maturing the organization’s ability to rapidly innovate in a highly regulated crypto-fintech space

Implemented observability across critical transaction flows (metrics/logs/traces, alerting, runbooks), shortening incident response cycles and reducing MTTR from ~45m to ~20m

Senior backend engineer at fast-growing tech startup. We are building a world class platform for software automation. Led products from ideation to deployment in production. Contributed to all products developed at the firm including developer tools for automation, serverless & containerized backends, custom automation orchestration service, and nextjs frontend application

Owned technical direction and delivery for the core automation platform powering primary business applications, balancing reliability, roadmap priorities, and extensibility for next-gen agentic solutions across multiple industries

Built AI-generative automation product (“Foundry”) leveraging shared libraries and prior patterns to generate new automation code; extended core infrastructure to support AI-agent workflows and cut time-to-prototype by ~50%

Partnered with business leaders to translate operational workflows into automation products (packaging, premium features, ROI-driven prioritization), leading cross-org delivery into human + machine processes

Contributed to a custom orchestration runtime capable of executing long-running automations across AWS ECS containers, AWS Batch, and AWS Lambda, supporting hundreds of agents and distributed workflows

Designed and delivered event-driven monitoring/telemetry services using EventBridge + SQS + Lambda, handling thousands of events/hour to report agent status, capture workflow telemetry, and enable operational alerting

Authored a Python telemetry SDK embedded in automations to capture step-level execution metrics and end-user signals, turning “black-box runs” into actionable analytics, proactive client alerting and reducing issue resolution time by ~30%

Led migration of an on-prem EDW to a cloud-native Azure lakehouse, enabling scalable ingestion and curated analytics for BI and Data Science while improving reliability and auditability

Architected multi-source ingestion across structured and unstructured inputs (APIs, FTP/file drops, and external feeds including web/social signals), supporting incremental refreshes and near-real-time updates across a growing enterprise dataset

Built Spark-based ETL/ELT pipelines on Azure (PySpark / Scala / SQL) processing multi-TB datasets for batch transformations and refresh cycles; improved pipeline runtime by up to ~80% via parallelism, partition strategy, and format/layout optimizations

Delivered a graph-based MDM capability using Neo4j/Dgraph + GraphQL and a React web app; implemented probabilistic entity resolution across sources to generate versioned “golden records,” improving match quality (precision/recall) to ~90% and reducing duplicates by ~20–40%

Implemented lakehouse zoning (raw → processed → curated) using Parquet + Delta, enabling ACID-style reliability, dataset versioning/time travel, and bi-temporal analysis; improved query performance by ~30–60% for analytics workloads and simplified audit/compliance reporting

Tuned and operated Spark infrastructure (autoscaling, partitioning, caching, file sizing, shuffle tuning), improving stability (failed jobs down 40%) through right-sizing and performance optimization

Provisioned and governed secure Data Science environments using Databricks / MLflow / Azure ML / VMs, implementing fine-grained access controls for internal + external teams; reduced environment setup time from days to hours while maintaining compliance controls

Implemented MLOps patterns for production ML pipelines: dataset/model versioning, containerized deployments, and promotion workflows; reduced model release cycle time by 50% and enabled consistent rollbacks/traceability for regulated workloads

Joined Hyatt Mobile First program : a redesign of current World of Hyatt mobile application to implement new in-stay functionality to improve guest experience and increase digital revenue share

Worked across multiple departments and cross functional teams to minimize risk when implementing solutions that impact service, data, application layers for Mobile.

Responsibilities:

Manage portfolio in agreement with Finance, IT and Business Stakeholders to scope the opportunity, determine benefits, and obtain sign-off by all impacted parties for developed features

Implemented process and quality improvements through a planned Process Improvement Plan- outlining a streamlined communication plan, divisions of responsibility, method-tailored agile release cycle for Mobile app releases and the change management process that locks in scope for each release

Design and implement availability, scalability and performance plans for AWS managed service environment between Mobile & Portal teams

Develop plans for deliverables, measure and track performance, forecast and predict costs, and analyze program data in order to enhance performance, achieve targets/benefits and identify opportunities to improve efficiency and effectiveness