Engineer consumer-facing data privacy and legal software products in Scala and Python; improve infrastructure and scalability by layering in effective monitoring, alerting, and testing

Implemented unit and integration test suites to add coverage for previously-untested codebases

Create core datasets necessary to enable strategic automation of data attribute detection, enable machine learning, and reduce manual input from data owners

Wrote monitoring framework for device data pipeline, receiving hundreds of petabytes of data daily

Implemented the Java backend of a service that calls to several internal APIs during the company’s annual hackathon

Coordinate with senior management and other engineers to set direction on opportunities to scale department’s testing and monitoring strategy; craft presentation materials to disseminate knowledge; work with core team to move ideations into internal tooling

Work closely with other groups like product, legal, business analysts, UX, and project management throughout delivery of solutions

Identify and monitor project and process risks; proactively scope timeline of technical improvements and establish risk mitigation and contingency plans

Deployed offline machine learning (ML) model into production to improve payment processor's decision making capability

Re-skilled to learn gRPC API framework, used to deploy online ML models to production

Architected and built multi-threaded Java application, run on AWS EMR with Kubernetes, that monitored data freshness to determine bottlenecks in the data pipeline architecture and measure SLOs to determine whether the team’s SLAs were being met.

Managed AWS EMR assets with Terraform; Infrastructure tooling development and security management with Terraform, Jenkins, and Docker

Test-driven development with Java, Python, and Ruby

Cloud Computing & Data Infrastructure Engineering with AWS, Spark, Hive, Hadoop & Airflow (cron job and Airflow operator development)

Built and tested malleable frameworks for ETL-ing petabytes of data at scale with lambda architecture (mix of batch and streaming)

Created self-service tooling and abstractions to help analysts, engineers and business users carry out lightweight ETLs, dashboards or monitoring

Mentored and onboarded engineers of varying levels to the data platform domain

Increased capacity across teams and within Airflow open source community by creating tooling that improved productivity