Data Engineer with 4 years building reliable data pipelines, warehouses, and analytics infrastructure at scale. I bridge the gap between raw data and business decisions — working closely with analytics, ML, and product teams.
“I write pipelines like I write code — modular, tested, and documented. If an analyst can trust the data, the pipeline matters. Observability and data quality checks are first-class citizens, not afterthoughts.”
Data Engineer with 4 years building and maintaining data infrastructure at scale using Python, Airflow, dbt, and Snowflake.
I started in analytics at a mid-size e-commerce company and kept getting pulled into the infrastructure side — fixing broken pipelines, rebuilding unreliable ingestion jobs, and wondering why the data was always wrong. Eventually I made it official. I've worked across retail, fintech, and SaaS, and I've learned that good data engineering is mostly about trust — making sure downstream teams can rely on what you ship.
CLI tool for scaffolding production-ready Apache Airflow DAGs from YAML specs, with built-in retry logic, SLA alerting, and data quality checks via Great Expectations.
Reference architecture for real-time financial event processing using Kafka and Flink, with a live React dashboard.
A collection of dbt macros for automated audit logging, freshness checks, and row-count reconciliation across Snowflake and BigQuery models.
2023-01-01 — Now
Senior Data Engineer
Senior data engineer on a 6-person data platform team at a Series B fintech company. Own core ingestion pipelines, the Snowflake data warehouse, and data quality infrastructure used by 30+ analysts and 3 ML engineers.
2020-08-01 — 2022-12-31
Data Engineer
Data engineer on a two-person data team at a mid-size e-commerce company. Built and maintained pipelines for marketing, finance, and operations analytics.
University of Washington — Seattle
B.S. Information Systems
2016-09-01 – 2020-06-01
Udacity
Data Engineering Nanodegree
2020-10-01 – 2021-03-01
I'm currently open to senior data engineering roles at data-driven companies. If you're looking for someone who can build reliable pipelines, improve data quality, and work closely with analytics and ML teams — feel free to reach out.
Location
Seattle, WA
Elsewhere