Open to opportunities

Christopher Stewart

@christopherstewart

Senior Data Engineer building lakehouse and real-time data platforms that drive revenue, reliability, and personalization.

United States

Message

What I'm looking for

I’m looking for a team where I can build lakehouse and real-time pipelines end-to-end—shipping reliable data products, improving latency and cost, and partnering cross-functionally to turn data into measurable growth and performance.

I’m a Senior Data Engineer who’s spent 10+ years building batch and streaming systems that power real-time pricing and personalization. At Airbnb (2017–Present), I developed a “Dynamic Pricing Intelligence Platform” processing 15B+ monthly booking and supply events using Spark Structured Streaming, Kafka, and AWS EMR to increase host revenue by 12%.

I also architected a “Search Ranking Feature Store” with Databricks, Delta Lake, and Redis, delivering near-real-time feature serving and reducing search latency by 38% while improving booking conversion by 6%. I’ve engineered Lakehouse architectures on S3 + Databricks, migrated Hive-based pipelines to Airflow + dbt, and built fraud-detection-ready ingestion pipelines with Kafka, Flink, and Kinesis at sub-5-second latency—backed by data quality and observability frameworks that reach 99.95% SLA compliance.

Experience

Work history, roles, and key accomplishments

Current

Senior Data Engineer

Current

Airbnb

Jan 2017 - Present (9 years 4 months)

Developed a Dynamic Pricing Intelligence Platform using Spark Structured Streaming, Kafka, and AWS EMR, processing 15B+ monthly booking/supply events to power Smart Pricing across 190+ countries and increase host revenue by 12%. Architected a Search Ranking Feature Store with Databricks, Delta Lake, and Redis, enabling near-real-time feature serving that reduced search latency by 38% and improved

Apache Spark Spark Structured Streaming Kafka Databricks Delta Lake Redis Snowflake DBT

Software Engineer

Accenture

Jan 2014 - Jan 2017 (3 years)

Engineered scalable enterprise applications using Java, Spring Boot, and RESTful APIs for Fortune 500 clients, supporting 1M+ end users across finance and retail domains. Optimized SQL workflows on Oracle and PostgreSQL (35% faster) and improved cloud CI/CD and release automation (40% shorter release cycles), reducing infrastructure costs by 25%.