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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.
About
Data Engineer with 4 years building and maintaining data infrastructure at scale using Python, Airflow, dbt, and Snowflake.
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.
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.
Currently building PipelineKit and exploring real-time streaming with Flink and Kafka.
Currently building PipelineKit and exploring real-time streaming with Flink and Kafka.
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.
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.
Skills
Languages
3 skillsdata engineering
4 skillsdata warehousing
3 skillsdatabases
2 skillstools & devops
4 skillsdata quality
1 skilldata & ML
2 skillsSelected Work
PipelineKit — Airflow DAG Scaffolding CLI
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.
- —Built a Python CLI using Click that generates fully configured Airflow DAGs from a YAML spec — including task dependencies, retry policies, SLA alerting, and Slack notifications.
- —Integrated Great Expectations checkpoint generation so every scaffolded pipeline ships with schema validation and row-count anomaly detection out of the box.
- —Added a local dev mode using Docker Compose that spins up Airflow, PostgreSQL, and a mock data source in under 60 seconds for rapid iteration.
StreamLedger — Real-Time Financial Event Tracker
Reference architecture for real-time financial event processing using Kafka and Flink, with a live React dashboard.
- —Built a Kafka producer simulating realistic financial transaction streams at 10k events/second, with configurable fraud patterns for testing downstream anomaly detection.
- —Implemented a Flink streaming job for real-time aggregations — rolling totals, per-merchant spend windows, and anomaly flagging — with sub-500ms end-to-end latency.
- —Wired a React dashboard with WebSocket updates to visualize live transaction volume, flagged events, and per-category breakdowns.
dbt-audit-macros
A collection of dbt macros for automated audit logging, freshness checks, and row-count reconciliation across Snowflake and BigQuery models.
- —Wrote a suite of dbt macros for automated audit logging — tracking row counts, null rates, and schema changes on every model run without custom Python.
- —Added freshness check macros compatible with both Snowflake and BigQuery that surface stale source tables before they silently corrupt downstream models.
- —Packaged as a dbt Hub-compatible package with full documentation and example project so teams can install and configure in under 10 minutes.
02.Work Experience
Meridian Analytics — Seattle, WA
2023 – Now
3yr 3moCurrentSenior Data Engineer
Jan 2023 — Present
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.
- 01Redesigned the company's core ELT architecture from ad-hoc Python scripts to a fully orchestrated Airflow + dbt stack, reducing data freshness SLA breaches by 90%.
- 02Built a data contract framework with Great Expectations that runs on every pipeline run — catching schema drift and volume anomalies before they reach downstream dashboards.
- 03Led a cross-functional initiative with analytics and product to define and document 80+ core business metrics in dbt, creating a single source of truth for company-wide reporting.
Novu Commerce — Portland, OR
2020 – 2022
2yr 5moData Engineer
Aug 2020 — Dec 2022
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.
- 01Migrated 14 legacy cron-based ETL jobs to Apache Airflow DAGs with proper retry logic, SLA monitoring, and Slack alerting — reducing weekly pipeline failures from 8-10 incidents to near-zero.
- 02Implemented a Snowflake data warehouse from scratch, consolidating data from Shopify, Stripe, Google Ads, and an internal PostgreSQL database into a unified analytics layer.
- 03Built a near-real-time inventory sync pipeline using AWS Lambda and DynamoDB Streams that reduced inventory discrepancy reports by 70%.
Education
B.S. Information Systems
University of Washington — Seattle
Data Engineering Nanodegree
Udacity
Contact
Making Data Teams Actually Trust Their Data
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.