Building reliable, scalable data infrastructure that makes analytics teams fast.

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About me

I'm a Senior Data Engineer based in Houston, TX with 5+ years of experience designing and building data infrastructure at scale. I specialize in cloud-native pipelines, dimensional data modeling, and modern data stack tooling across Snowflake, dbt, Spark, and Azure.

I care deeply about data reliability — pipelines that don't just run, but run correctly — and I've led engineering on projects spanning real-time ingestion, lakehouse architectures, and CI/CD automation for data platforms.

Outside the terminal: publications, awards, and judging industry work keep me connected to the broader data community.

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Tech stack

Cloud platforms

Orchestration & transformation

Languages

Data & streaming

DevOps & tooling

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Featured projects

🏗 Medallion Lakehouse Pipeline

End-to-end Bronze → Silver → Gold architecture on Delta Lake with PySpark. Implements SCD Type 2 dimension management, data quality checks via Great Expectations, and Airflow orchestration.

PySpark Delta Lake Airflow Great Expectations Kimball modeling

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📦 dbt + Snowflake Analytics Platform

Production-style dbt project with staging → intermediate → mart layers, 100+ data tests, automated documentation, and a CI/CD pipeline via GitHub Actions that runs on every pull request.

dbt Snowflake GitHub Actions Data contracts Star schema

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⚡ Real-Time Streaming Pipeline

Kafka → PySpark Structured Streaming → Apache Iceberg. Processes live event streams with exactly-once semantics, schema evolution, and a lightweight monitoring dashboard.

Kafka Spark Streaming Iceberg Python Confluent Cloud

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🔍 Data Quality Observability Framework

Pluggable data quality layer built on Great Expectations + dbt tests + custom alerting. Includes data contract definitions, anomaly detection, and a quality score dashboard per pipeline.

Great Expectations dbt Python Data observability Data contracts

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🤖 LLM Data Pipeline (RAG Architecture)

Ingestion and processing pipeline for unstructured documents into a vector store for retrieval-augmented generation. Covers chunking strategies, embedding pipelines, and evaluation metrics.

LangChain pgvector Python Airflow OpenAI API

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GitHub stats

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Currently

• 📖 Working through Fundamentals of Data Engineering (Reis & Housley) and Designing Data-Intensive Applications (Kleppmann)
• 🛠 Building out a production lakehouse portfolio with Delta Lake and dbt
• 📊 Deepening expertise in Apache Iceberg and streaming architectures
• 🎯 Targeting Senior Data Engineering roles at high-growth, equity-granting tech companies

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_{Open to Senior Data Engineering opportunities · Remote preferred · Let's connect on LinkedIn}